Collaborative Decision Analysis

Literature Review of Collaborative Decision Analysis

State of the Art Paper

Connie White

New Jersey Institute of Technology (NJIT)

University Heights

connie.m.white@gmail.com

Spring 2008

SOTA Committee

Advisor: Murray Turoff

Committee Member: Starr Roxanne Hiltz

Committee Member: Bartel Van de Walle

Sota Committee:

Murray Turoff

Starr Roxanne Hiltz

Bartel Van deWalle

“There are no more promising or important targets for basic scientific research than understanding how human minds, with and without the help of computers, solve problems and make decisions effectively, and improving our problem-solving and decision-making capabilities.”

Herbert Simon

BRIEF CONTENTS

CHAPTER 1: INTRODUCTION

CHAPTER 2: DECISION MAKING

CHAPTER 3: BIAS

CHAPTER 4: DECISION SUPPORT SYSTEMS

CHAPTER 5: VOTING

CHAPTER 6: DELPHI

CHAPTER 7: WHAT WE KNOW AND WHAT WE DON’T KNOW

LIST OF TABLES AND FIGURES

ACRONYMS AND ABBREVIATIONS

REFERENCES

TABLE OF CONTENTS

CHAPTER 1

INTRODUCTION

1.1 Objectives

1.2 Overview

CHAPTER 2

Decision Making

2.1 Abstract

2.2 Introduction

2.3 Differences

2.4 Processes

2.5 Groups

2.6 Task Types

2.7 Heuristics

2.8 Discussions

2.9 Conclusions

CHAPTER 3

BIAS

3.1 Abstract

3.2 Introduction

3.3 Definitions

3.4 Bias Through Framing

3.5 Bias in Individuals and Groups Making Decisions

3.6 Bias in Bounded Rationality and Uncertainty

3.7 Information Overload and Bias

3.8 Bias in Experiments

3.8.1 Systematic Bias

3.8.2 Experimenter’s Bias

3.8.3 Response Bias

3.9 Conclusions

CHAPTER 4

DECISION SUPPORT SYSTEMS

4.1 Abstract

4.2 Introduction

4.2.1 Acronyms

4.2.2 Decision Support System Components

4.2.3 Database Management System

4.2.4 Decision Support Systems Defined

4.2.5 Wisdom and the Human Component

4.3 Design Contingencies

4.3.1 Size Matters

4.3.2 Proximity

4.3.2.1 Face-to-Face (F2F)

4.3.2.2 Partially Distributed Groups (PDTs)

4.3.2.3 Virtual

4.3.2.4 Online Communities

4.3.3 Problem Types

4.3.3.1 Structured

4.3.3.2 Semi-structured

4.3.3.3 Ill-structured

4.3.3.4 Tame

4.3.3.5 Wicked

4.4 Varieties of DSS

4.4.1 Computer Mediated Communication Systems (CMC)

4.4.2 Single User Decision Support Systems

4.4.3 Group Decision Support Systems (GDSS)

4.4.3.1 Levels of GDSS

4.4.4 Distributed Group Support System (DGSS)

4.4.5 Social Decision Support Systems (SDSS)

4.5 Information Overload

4.5.1 How It Happens

4.5.2 What Occurs From It

4.5.3 How To Decrease It

4.5.3.1 Data Management

4.5.3.2 Social Influence

4.5.3.3 Conferencing Systems

4.5.3.4 System Induced Measures

4.6 Process Catalyst

4.6.1 Leader

4.6.2 Facilitator

4.6.3 Moderator

4.6.3.1 Motivation

4.6.4 Chauffer

4.6.5 Automated

4.7 Methodologies

4.7.1 Unidimensional Data

4.7.2 Multi-criteria

4.7.3 Measuring Decision Support

4.8 Conclusions

CHAPTER 5

VOTING

5.1 Abstract

5.2 Introduction

5.2.1 Voting for Decision Making

5.2.2 Definitions

5.3 Voting Methods

5.3.1 Plurality

5.3.2 Plurality with Elimination

5.3.3 Borda Count

5.3.4 Pairwise Comparison

5.4 Fairness Criterion

5.4.1 Majority

5.4.2 Condorcet

5.4.3 Monotonicity

5.4.4 Independence of Irrelevant Alternatives

5.5 Arrow’s Impossibility Theorem

5.6 Protocol

5.6.1 Robert’s Rules of Order

5.7 Roles

5.8 When to Vote

5.9 Bias in Voting

5.10 Uncertainty

5.11 Anonymity in Voting

5.12 Conclusions

CHAPTER 6

DELPHI

6.1 Abstract

6.2 Introduction

6.3 Characteristics of Delphi

6.3.1 Anonymity

6.3.2 Feedback

6.3.3 Statistical Group Response

6.4 Delphi Processes

6.4.1 Early Delphi Processes

6.5 Problems with Delphi

6.5.1 Experiments

6.5.2 Subjects

6.5.3 Consensus

6.6 Bias

6.7 Experts

6.7.1 Weights

6.7.2 Self-Rating

6.8 Experiments

6.8.1 Groups

6.8.1.1 Large Groups of Experts

6.8.1.2 Subgroups of Experts

6.8.2 Rounds

6.8.3 Asynchronous

6.8.4 Uncertainty

6.8.4.1 Missing Data

6.8.4.2 Interpretation

6.9 Conclusions

CHAPTER 7

WHAT WE KNOW AND WHAT WE DON’T KNOW

LIST OF FIGURES AND TABLES

FIGURES

2.1 Dynamic Decision Making

2.2 Support Levels and Dimensions

4.1 Architectural Support of the DDM

4.2 Different Settings for GDSS

5.1 Factors Influencing Opinions

TABLES

2.1 Group Dynamics

2.2 Task Contingencies

2.3 Validation Contingencies

2.4 Coordination Contingencies

ACRONYMS AND ABBREVIATIONS

AHP Analytic Hierarchy Process

CMC Computer Mediated Communication System

CSCW Computer-Supported Cooperative Work

CS Conferencing Systems

CSS Collaborative Support Systems

DDM Dialog, Data and Modeling

DGSS Distributed Group Support Systems

DM Decision Makers

DSS Decision Support System

EMS Electronic Meeting Systems

GDSS Group Decision Support System

GSS Groups Support Systems

IBIS Issues Based Information System

ISCRAM Intelligent Systems for Crisis and Response Management

MAUT Multi-Attribute Utility Theory

MCDM Multi-Criteria Decision Making

MMP Multiobjective Mathematical Programming

SOTA State-of-the-Art Paper

CHAPTER 1

INTRODUCTION

Do you think that I know something you don't know?

What do you want from me?

If I don't promise you the answer would you go?

What do you want from me?

- Pink Floyd

1.1 Objectives

The purpose of the State-of-the-Art paper (SOTA) is to examine the literature relevant to the study of collaboration in decision making. In particular, the focus is on large groups of experts working together using computer mediated communication systems (CMC) producing group opinions. These individuals are not collocated and are distributed throughout the world working together as virtual teams on particular parts of a larger problem. The idea behind this strategy is that individuals can work together on subproblems concerning their particular area of expertise, dividing the problem into its atomic parts.

1.2 Overview

Difficulties arise when large groups of experts work together using collaborative decision making software systems. In particular, how these individuals work together in a decision making process is analyzed. Individual experts working together have their opinions independent from one another, but the outcomes are a collaborative effort reflected by a single opinion calculated from multiple inputs. Experts may or may not have knowledge in an area so they may or may not be able to contribute to a problem by offering solutions or input and these issues are explored. This is no trivial task as many issues arise during these efforts. Individuals can have bias towards a particular interest, uncertainty in their decision making or have no opinion on a given domain due to their lack of knowledge in that particular area.

Many issues arise during the decision making process which have been identified and are described in a variety of ways in the literature. In the second chapter of this SOTA, processes of decision making are explored and are defined multiple ways in the literature. Group dynamics along with task types are identified and the heuristics used in the cognitive process dealing with information overload are presented. Chapter 3 explores numerous ways in which bias can affect decision making throughout the entire process with both individuals as well as groups of individuals. Bias is identified in how problems are framed, as a result from information overload and in experimental research. Individuals suffer from bias as do groups. Biases can be recognized in many stages of the experimental process and some of these are identified. In Chapter 4 decision support systems are explored and analyzed for their ability to aid individuals in the process. The focus is on how the group opinion is formulated utilizing voting methods in Chapter 5. Different voting processes are researched, protocols are delved into and Arrow’s Impossibility Theorem is covered. Chapter 6 defines the Delphi technique, its processes and how it has been used in the past. What constitutes an expert and problems with experimental outcomes are explored. This SOTA ends by posing questions that are raised given the issues that are identified in the literature review.

CHAPTER 2

DECISION MAKING

2.1 Abstract

Individuals make decisions and groups of individuals work together to solve problems and make decisions. Groups can have consensus or conflict and problems can be addressed given the type of task being considered. Decision making is a very complex topic with many avenues in which to wind down. In keeping within the scope of this paper, only particular areas of decision making will be addressed for this is an ever intriguing area which could never feasibly be completely covered in one writing. Herbert Simon divides this type of group interaction into two areas where he categorizes the activities of “fixing agendas, setting goals and designing actions” to that of problem solving and “evaluating and choosing” to that of decision making. Throughout this paper, I will assume these to be both part of the decision making process and use them interchangeably. Problem solving can be bombarded by the enormous amount of information that can be relevant to the given situation. In order to have effective decision making outcomes, the group dynamics and information needs to be managed in a way that is conducive for the best result given the way people work together and how they think. Humans have numerous, primarily misguided ways of processing massive amounts of information. Due to time, information and other bounded factors, conducting an exhaustive search of all available solutions is not a possibility. The search must be restricted such that only the best information is considered and that non-pertinent information is discarded.

2.2 Introduction

There are many definitions of decision making to be found in the literature (Simon, 1969, Hiltz and Turoff, 1978, Turoff, 1996, 1998, Whitworth and McQueen, 1999, Druker, 2001, Nutt, 2002, White, et. al, 2007). Although each version has its own particular language used outlining the steps defining the process of decision making, there is a common set of functionalities. They are all more or less defined as such:

1. Setting the agenda, recognizing the problem;

2. Classify/define/represent/assess the problem;

3. Develop list of solutions, find alternatives, list boundaries;

4. Choose a solution;

5. Some sort of feedback (in some), evaluation process, test the solution against end result.

Various researchers’ approaches can make this list differ greatly as to the outcome or even, how the process is managed. For example, one task in any decision problem is to draw up a list of the possible actions that are available. Some believe that considerable attention should be paid to the task of ‘list generation.’ This is because the choices of action will be limited to those contained within this list (Simon, 1969). A theory is to designate that a list of all possible solutions is important to identify, one which exhausts the possibilities (Lindley, 1971). Others may argue that this is not an optimal approach due to reasons described as bounded rationality (Simon, 1969). The same basic theory holds true with muddling through – this is where a feasible subset of possibilities is suggested as the optimum and only realistic way to effectively select an optimal solution (Lindblom, 1959, 1979) where incremental changes are made to the present given situation, aiding in fine tuning and improving the imperfections versus implementing opposing alternatives as knee jerk reactions to bad situations.

Simon characterizes the intelligence gathering phase which he refers to as ‘finding alternatives,’ as holding the key to knowing how it’s to be handled through the level of understandability. The more deeply one understands the problem, the more efficient the means to the solution will be (Simon, 1969). Gaining a better understanding of the problem can be derived by discussions between team members. Increased discussion lessens ambiguity and lessens conflict, increasing consensus (Turoff and Hiltz, 1978, White, et al, 2007a).

Many other factors can influence how individuals or groups interact to make more optimal decisions. Certain implementations of features are found to be conducive to improving the decision making process. These features implemented into a system can increase the participation amongst team members and can offer asynchronous interaction to generate an environment that will produce collective intelligence (Hiltz and Turoff, 1975). These are listed as:

1. “Anonymity;

2. Independent generation of ideas or judgments, by assuring that all participants have an opportunity to think and record their ideas or judgments before receiving the ideas of others;

3. Specification of modes of communication for some or all the communication i.e. the use of written communications;

4. Mechanisms for assuring equality of opportunity to participate;

5. Appointed facilitators to assure the flow of communications in the prestructured manner (rather than reliance on informal leadership from within the group itself);

6. Specification of allowable subjects of and forms of communications (example: voting or discussion segregated by time periods);

7. Some sort of organized feedback to the group of the input of each member and the aggregate ‘group decision’ that is emerging;

8. Specification of allowable ‘who-to-whom’ patterns of communication (no private communications)” (Hiltz and Turoff, 1978, pp. 282-283).

2.3 Differences

One variable that plays differently from one methodology and researcher to the next is the manner in which the decision process is allowed to proceed. For example, some of the steps are to be fulfilled at particular times in a discrete, synchronous manner while others are dynamic and offer an anytime/anywhere asynchronous environment (Simon, 1969, Hiltz and Turoff, 1978, Turoff, 1996, 1998, Whitworth and McQueen, 1999, Druker, 2001, Nutt, 2002, White, et. al, 2007b).

Table 2.3 Validation Contingencies

Table 2.4 Coordination Contingencies

Rana, Turoff and Hiltz, (1997), identified support levels, one for the individual, a second for processes and a third for meta processes, along with their functionalities along each dimension. Tasks fall along this functional paradigm within the 3 dimensions which further explain and correspond to their needs, further determining other architectural information that will advise complementary needs and requirements.

Figure 2.2 Support Levels and Dimensions

An objective is to “characterize and empirically validate the differences in group interaction process that would be expected among task distinctions that can be conceptualized along three dimensions of the task classification framework” (Rana, et al, 1997). The TTI offers a structure from which other GSS studies can be based to measure the fit between task and technology.

As a conclusive statement concerning tasks, it appears as though through the discovery of the task type, the engineering of the system can be made. The task type defines the needs of the system which in turns defines the needs of the user. So, to define the task type, means to define the type of system that will be needed in order to enhance productivity in groups.

2.7 Heuristics

Voter decision making creates a high demand on cognitive resources (Robertson, 2005). To deal with this problem, voters use information-seeking and decision-making heuristics to ease the cognitive tasks. In this view, giving high weight to a position checklist, a candidate’s appearance, or a physical endorsement is simply a way to shortcut a more complex cognitive process. Lau et al proposed that heuristics guide information seeking behavior and they provide evidence that the use of heuristics in political decision making is correlated with retention of important information and even the making of more informed choices.

2.8 Discussions

When is it beneficial for a group to have a discussion? Turoff and Hiltz (2002) indicated that through group discussion, ambiguity would lessen and understanding would increase. Discussion increases the domain of knowledge within the group members but at the same time, increases the chance of disagreement and reduces groupthink (Whitworth, et al 1999). How is it different in a face-to-face setting versus an online setting where the members were not collocated? Benefits are described by Whitworth, et al (1999) indicating that online participation utilizing a voting technique would lessen non productivity that is a result of group dynamics when there’s conflict. But also, they demonstrated where there is more focus on the materials being presented and its merit versus any intergroup conflict that may get things off the track or from conversations that digress and lead astray wasting time. These are some more problems with face to face meetings.

2.9 Conclusion

We have seen many different suggestions for problem solving, decision analysis and decision making structures.

Many of these have both unique steps and/or overlapping similarities. What is clear is that one cannot separate a detailed view from the type of problem, task, or decision issue and the presence or absence of certain properties:

1. Degree of innovation needed;

2. Types of constraints (e.g. Social problems);

3. The nature of evidence required;

4. And the Interdisciplinary nature, etc.

Decision making is a complex problem given the many variables that have to be taken into consideration when evaluating which process best reflects a group’s opinion on any given issue. How does one evaluate the criteria behind the meaning of ‘best reflects?’ How is ‘best’ defined? This may be dependent on the given situation. For example, in some cases, the group could define the criteria upon which to base their definition of ‘best.’ In others, groups of experts may be external to a problem per se, but may have abstracted enough scenarios to justify a model upon which to base judgment after an event has played out. Yet in other cases, the problems for decision making are wicked and can never be systematically evaluated. It’s these sorts of cases, or events in which decision making is at present challenged and upon which group collaboration in particular, needs to be further explored. Therefore, we believe as part of the research in this area, an adequate problem solving structure has to be tailored to the nature of the problem and the environment in which it occurs.

CHAPTER 3

BIAS IN DECISION MAKING

Just the facts ma’am, just the facts.

Sgt. Joe Friday, Dragnet

3.1 Abstract

This section reviews bias on the part of individuals and groups in decision making. Literature is reviewed, bias is defined and issues are outlined demonstrating the negative effects bias can have and why it should be eliminated from decision making in order to have better decisions made which are more reflective of the goals that are desired. The problem with bias in decision making is that it can ignore the best criteria being presented, skew information and thus, lead to decisions based on information that should be ignored which lead to poor end results. Methods and strategies are explored showing how to best eliminate bias and strengthen group performance.

3.2 Introduction

Individuals can have bias in decision making which can skew an outcome based on these personal influences. Personal bias can be in the form of race, ethnicity, religion, political party, family name, predisposed situations based on past experiences, i.e. internal influences. It can be based on ethics, department affiliation, group affiliation, geographic regions, and nationalities, i.e. external influences. Recently, a British teacher working in Sudan allowed her students to name a Teddy Bear Mohammad. She was charged and convicted of blasphemy by the government. People judged the incident with great bias where beliefs were mixed on the punishment fitting the crime; on one side of the issue, calls for the teacher’s death were made; On the other, opinion was that there was a gross overreaction to a cultural faux pas. When bias enters into judgment, how does this affect the correctness of the decision to be made?

”These are differences that cannot be corrected or changed easily or by applications of a greater philosophy or belief unless both parties accept it.” [1] Some bias can be controlled, some cannot. The functionalities available to the group members may eliminate some forms of bias, but other forms such as the ones described here, are based on beliefs, interests and other factors that are inherent to individuals. And to judge others favorably who have similar beliefs, desires and interest as those of our own, is a natural occurrence (Simon, et al, 1986).

In the remainder of this chapter, I will cover various definitions of bias then, show the various types of bias likely to be encountered during this research. Everything from the experimenter’s bias, to the way in which data is collected, disseminated, framed and the method of choosing the right group, to bias in decision-making due to missing or incomplete data will be introduced. Last, I explore systematic bias in decision making and how to deal with it appropriately during statistical analysis.

3.3 Definitions of Bias

Bias is found to be defined a number of ways, most of which use the common adjective of ‘prejudice’ with a negative connotation. Bias is defined as follows by a variety of references:

Herbert Simons believes that we don’t know how we make up our minds under uncertainty. He writes “In many cases, they can predict how they will behave, but the reasons people give for their choices can often be shown to be rationalizations and not closely related to their real motives. At times, the observation of a new problem can be the stimulus where decisions stir past experiences which may reside deep within the psyche and surface only when a set of extenuating circumstances occur which triggered the memory” (Simon, 1986). Next, I will cover a few ways in which bias is reflected in decision making and problem solving.

3.4 Bias Through Framing

Information can be perceived in a particular manner if the information is framed as positive or negative or skewed in some way. Tversky and Kahneman referred to this as a judgmental heuristic called availability, “Biases due to the retrievability of instances” (1974). This is an example of how bias is an error in decision making because in this case, there is a high level of association with the selection being made, although the choices are equally likely and this skews the information making you only more likely to pay attention to what you know and ignore the other given information. During a trial, evidence can be presented by experts from the same exact fields, but the information will be presented to the jurors in a biased fashion due to the side reflected by the prosecution or defense, that the expert is representing. Which one is true? Can they both be true or can the truth be sought from both? How best can the jurors make a correct decision based on the evidence before them? It is well researched that information can be framed negatively or positively which can influence or distort the information and have a direct effect on the outcome of the decision (Mathews, et al, 1998; Murphy, 2001).

Experts as well as laymen are influenced by information framing (Ericsson and Staszewski, 1989). Mathews, et al conducted a study and demonstrated that both experts and non expert’s forecasting abilities are bias favorably when the information is framed in a positive manner. The same information presented in a negative light gave different results. However, it was further shown that positive information is processed differently cognitively and forecasts were overestimated given positive information versus the equivalent negative information (Mathews, et al 1998) although the experts were found to be more accurate in their judgments. They concluded that framing should be taken into consideration when experts are presented the task of forecasting.

3.5 Bias in Individuals and Groups Making Decisions

Individual bias can be minimized when larger groups participate in the decision making efforts and greater accuracy is reflected according to Condorcet’s Jury Theorem (Berg, 1993; Condorcet, 1976; Hiltz and Turoff, 1978). This is one of the primary reasons behind groups making key decisions for policies, laws, verdicts and other important judgments where a best decision must be made for the greater good of man or where the criteria pertinent to the situation requires the need to be evaluated critically and without bias using a group of representatives. However, it was noticed that the error from bias still could occur given these groups may be trained in the same manner, working for the same people or have other similar characteristics which over taint their overall group opinion (Bottom, et al, 2002). People who are trained the same way are likely to vote the same way.

People demonstrate bias by thinking if something worked before, it will work again. If problems are static, this may be true. However, dynamic situations call for different decisions to be made and the environment could have changed. Some decisions are made based on the fear of change and how it may be unknown to a user. People resist change as do many organizations. Sometimes there may be risk in the implementation of some selection. Risk can be a factor which promotes bias in that the organization finds comfort in its routine and past methods. There are cycles of some solutions being in fashion and thus, promote bias in choice based on ‘what is in’ at the time. Other organizations are biased in using their own creations versus something else that may have a better fit (Turoff, 2000).

3.6 Bias in Bounded Rationality and Uncertainty

Decision making has its problems with information overload amongst flawed or biased individuals who work together. Uncertainty in the data, missing data and unknowns add to the complexity of how bias is handled. Bounded Rationality states that people can only process so much information due to human limitations of the brain’s capacity to acquire, maintain and process data. Simon turns to the SEU model for answers but this is not a system meant for a dynamic environment with imperfect information.

Other forms of bias come from bounded rationality where information may be limited and time of the essence and all alternatives cannot feasibly be explored (Gigerenzer, 1999). There may be ignorance to the subject matter on some part of the individuals participating not knowing all alternatives and thus the choice selected on the basis of recollection. Bias is inherently reflected in each individual, but as a group, especially as the group grows in numbers, is diluted and the decisions makers not only overcome the bias with a canceling out effect, but actually ascertain a better overall decision than any one group member could have provided (Condorcet 1976; Ericsson, et al, 1989; Turoff and Hiltz, 1978).

How individuals handle this uncertainty is through a multitude of heuristics that prove to be somewhat error prone. Tversky and Kahneman (1974) mention bias that exists even when the statistical data tells the human otherwise, that certain aspects of the mind form of heuristics which override bad or good information. For example, someone may judge a human to be a librarian due to a set of characteristics that the human has with the stereotype of a librarian. Given the added information that 70% of the sample populations are engineers, the human will still use the heuristic of categorization and ignore other information. This is also an example showing where humans think differently than thinking algorithms such as Bayes Theorem. Bayes would have factored in that there was the 70% chance of the person being an engineer where the human ignored the information and stayed with their categorization (Tversky and Kahneman, 1974).

Bayes theorem has been used when prior probabilities are large and other information has been gathered from a database. Working with a priori knowledge, decisions of the future can be based on the past. Another favorable reason to use Bayes is that the decisions can be traced along a tree structure where the paths can offer the decision makers an explanation for the end result. It’s this ‘explaining away’ of the decision that is attractive to humans. Although this is a useful way to implement a thinking algorithm into a computer, this is not the way all humans think and should not be used for group decision making. “…individuals do not always process information in a manner consistent with Bayesian models” (Bottom, et al, 2002, pg. 154).

An observation was made revealing how more primitive societies make decisions. The methodology was a reflection of collective wisdom combined with randomness into new alternatives. The method of the natives was nonrandom randomness where a series of influences into a bone gave elders a path from which more information could be deduced. It’s argued that humans are not good random generators and tend to make the same mistakes repeatedly. So, it can be said that when decisions are made ‘off the beaten path,’ then the other choices available may also be better (Moore, 1957). There is a bias in dealing with the unknown that lies deep within the psyche of the elders that together, with the randomness of the process, comes new ideas from old memories from which to interpret a successful alternative idea.

Bottom, et al demonstrated that when partially informed individuals worked together, they performed at an amazing level of accuracy as a group. A majority rule method was used and outcomes were good when groups worked on simpler problems. However, on more complex problems, this approach did not seem to remove the individual bias which was considered more threatening in these types of outcomes. This research also showed where such optimal heuristics such as Bayes Theorem, worked in some areas but did “leave the individual susceptible to certain, predictable and sometimes quiet profound forms of bias” (Bottom, et al, 2002, pg. 149).

3.7 Information Overload and Bias

When there is too much information to be processed, individuals make decisions which suffer from systematic bias. Basically, people can be presented so much information that they no longer are capable of making the correct choice, but revert back to what they know or perhaps select what they recognize (Tversky and Kahneman, 1974). Gigerenzer, et. al., argue that statistical models of reasoning and information presentation are not best in aiding human decision making. They argue that other natural formats are better for understanding and solving problems where it concerns the interpretation of data.

Information overload can be reduced when the subjects are allowed to select and focus on a subgroup of choices (Lindblom, 1959, Simon, et al, 1986). They may be perceived as more relevant information from which to choose and aid in reducing the time it would take in making an exhaustive search of all available information and paths to take in evaluating all possibilities. Having the capability in a system allows much needed flexibility for a group to be able to select a subgroup of issues from a larger list (Lindblom, 1959). The lessening of the selection reduces the load on the person. This would aid in reducing the number of heuristics that may be employed in areas as mentioned previously which are prone to induce error into the judgments being made by the individuals.

3.8 Uncertainty and Bias in Heuristics Used

When decisions are made and there is uncertainty in that data from which to ascertain a final conclusive answer, heuristics are used. These can lead to major errors in decision making. Tversky and Kahnemen (1974) claim that people making decisions under uncertainty use poor heuristics and ignore factors that lead to major errors in decision making. Best stated by Herbert Simon (1986), “Some of the general heuristics, or rules of thumb, that people use in making judgments have been compiled – heuristics that produce biases toward classifying situations according to their representativeness, or toward judging frequencies according to the availability of examples in memory, or toward interpretations warped by the way in which a problem has been framed.”

3.9 Bias in Experiments

Bias can come out even during the experiment. There’s experimenter’s bias whereby a scientist can hope for results to be favorable towards their assumptions. So, there is the problem that the information can be evaluated in a bias manner and therefore, not precise and true. Even the subjects in the study can react differently than they would have in reality due to the expectations of them as individuals in the research project (Simon, et al, 1986). Bias can come in the form of questions on a survey, where methods must be in place to not have confounding factors, like ‘yea saying’ for example where the questions are on a somewhat alternative basis of being framed more negatively then positively. Anonymity can aid in removing bias if an individual is working with a group. If the members of the group are anonymous to one another, a truer opinion can form from the individuals, and hence from the overall group as to how they truly feel about some agenda. Anonymity removes fear of repercussion due to a conflicting opinion, it removes preconceived ideas which may come from a member, and it removes peer pressure from having to vote a certain way in conformity.

3.10 Systematic Bias

Systematic bias arises from inaccurate measurements. Best described by Hamburg, (1970)

“Among the causes of bias are faulty design of a questionnaire such as misleading or ambiguous questions, systematic mistakes in planning and carrying out the collection and processing of the data, nonresponse and refusals by respondents to provide information and too great a discrepancy between the sampling frame and the target universe.”

There are many problems with systematic bias as they are not corrected by the size of the population increasing. They can be corrected by using an alternative means of measurement and through those results attempt to clue researchers as to where to look for the inconsistency.

3.11 Experimenter’s Bias

Experimenter’s bias is when the researcher has some sort of influence on the outcome due to his/her expectations of results. Experimenter’s bias or any other research which is put in a place of influence be it conscious or subconscious must be eliminated as much as possible to not influence the outcome of the results. At conferences, there’s a double blind review for most peer reviewed article submissions. This has two reviewers read and rate an article based on some criteria, with neither reviewer knowing the identity of the author(s). This is to remove any bias that may come with knowing who wrote the article, good or bad and provide rigor for better conferences. A double blind review is one way to eliminate experimenter’s bias.

3.12 Response Bias

Response bias was addressed in the way surveys were distributed to subjects by Hiltz and Turoff. They claimed that anonymity was shown to increase response rates. They used this result to support the idea of anonymity in a computerized conferencing setting stating that, ”we find indications that, since CC responses would be entered in private, uninfluenced by a desire not to offend an interviewer, and can be made anonymously, for “sensitive” issues at least, more truthful and fuller accounts may be elicited than by face-to-face or telephone interviewing” (Hiltz and Turoff, 1978, pg 264). Giving the subjects anonymity in their responses gets a truer set of data for the researcher and is another way to minimize bias in decision making.

3.13 Conclusion

We have demonstrated that bias can come in a number of forms, from individuals, to groups, to the system and how the information is framed. Strategies have been laid out in order to demonstrate what can be done to reduce bias where it’s rational to believe that it can be reduced. The goal is to have the best decision making possible amongst team members, according to whatever it is that defines ‘best.’ Groups should be offered a system which best reflects their opinions and reduces bias as much as humanly possible. In this research effort, there will be a mission to identify and eliminate bias, wherever possible. In all of the steps of the research process, attempts will be made to eliminate bias so that the optimum judgment can occur given the information at hand.

CHAPTER 4

DECISION SUPPORT SYSTEMS

It is a long way from data to wisdom.

Zeleny

4.1 Abstract

This chapter reviews Decision Support Systems exploring areas of design which have influence on the outcome. A non-exhaustive literature review is conducted determining areas which should receive focus when given the task of building a system of this nature. Group Support Systems come in many varieties given the users proximity, group size and the task to undertake. Information overload is analyzed as to how it happens, how the users deal with it and then methods to decrease it are described. Catalyst in the form of facilitators and moderators and such for a group process is reviewed where different variations fit different needs. Complexity concerning the number of dimensions considered in a problem is covered. We hope to cover many of the topics that are vital in the development of a decision support system. This is a very large area and is briefly covered here.

4.2 Introduction

There can never be enough emphasis on making the best decision possible and continuing to improve it (Simon, 1960, Weick and Sutcliffe, 2001, Eom, 2001). The ramifications of decisions can be never-ending and they set the stage from which to work and from which the next decision is to be made (Weick and Sutcliffe, 2001, Eom, 2001, White, et al, 2007a). Building a system to support decision making includes considerations to support all of the phases for it (Hiltz and Turoff, 1978, Sprague and Carlson 1982, Turoff, et al, 1993, Eom, 2001, Turoff, et al, 2002, White, et al, 2007b). Present day decision support systems are used for scenario ‘what if’ generation and evaluation and also for generating and analyzing a means to an end type of ‘goal-seeking’ strategy into the phases of design and selection (Eom, 2001). To support any group there needs to be a strong foundation to support both communication and coordination processes (Turoff, et al, 1993).

4.2.1 Acronyms

There are many acronyms where it concerns the use of technology in the creation of decision support systems (DSS). Many of the systems have overlapping definitions and this can cause confusion to the point where multiple systems are erroneously referenced as one. There’s computer-supported cooperative work (CSCW), group support systems (GSS), computer mediated communication systems (CMC), collaboration support systems (CSS), conferencing systems (CS), electronic meeting systems (EMS) and group decision support systems (GDSS), just to name a few (Hiltz, et al 1996, Eom, 2001, White, et al, 2008). These are not interchangeable, but are specialized based on a particular set of requirements (Hiltz, et al, 1996). For example, GDSS have focused on individuals working together in decision making and problem solving, while CSCW focuses on improving communication between group members (Hiltz, et al, 1996, Eom, 2001).

4.2.2 Decision Support Systems Components

Decision Support Systems can be categorized initially into two areas which separate the human from the system. Although these two entities interact, each must be first understood as to what each is capable of as well as what each other’s limitations may be (Simon, 1969, Hiltz and Turoff, 1978). The system component can then be further broken down into two more sub-categories, one representing how the data will be handled, and the other for the methods and models that will be used by the system (Sprague and Carlson,1982). All DSS have a common framework referred to as the Dialog, Data and Modeling (DDM) paradigm. This states that all decision support systems are based on the following:

1) Dialog exchanged between the users of the system,

2) The Data utilized and managed in the system and

3) The Model which supports the analytical work.

A model was designed by the authors and is presented in Figure 4.1 (Sprague and Carlson, 1982). This model reflects the architectural support of the DDM paradigm for a DSS. Each system is modified and specialized according to the type of activity that will be performed by the users. This task-technology, best-fit claim, is also supported by others (Hiltz, et al, 1996, Zigurs 1998).

Figure 4.1 Architectural Support of the DDM

4.2.3 Database Management Systems

This model shows the three components connected using a database management system (DBMS). The components are defined as the dialog component which connects the user to the system for interactive purposes, the data component which stores the data through input , archiving or some other means and the model component, which will have the algorithms and problem-solving analysis tools required by the users of the system (Sprague and Carlson, 1982). Database Management Systems come in all shapes, forms and sizes. There are free- and open-source systems available with no support, or very sophisticated expensive systems with a great deal of support, ranging from small on-site jobs to very large distributed jobs (White, et al, 2008a). DBMS are the backbone of the information provided to the end user. Queries are made against the data to form information. This information can feed into other programs with algorithms necessary to provide various analyses to further aid the decision maker (Gehani, 2007, Rob and Coronel, 2007). Providing a well designed interface between the human and computer is crucial (Hiltz and Turoff, 1978). Great consideration should be given to the availability of input/output devices, peripheral hardware available, presentation of materials in the form of reports, and many other issues which go beyond the scope of this paper.

4.2.4 Decision Support Systems Defined

As demonstrated, there are a multitude of topics used to define decision support systems. However all of the definitions, either in-part or in-full, have the common characteristics of a procedure for people which uses technology in order to support decision making (Alter 1980; Bonczek et al. 1981; Keen and Scott-Morton 1978; Sprague and Carlson 1982). Primarily, the system is supposed to support the user and not replace them. The system uses data and models in order to aid in making more informative, and/or better decisions. The DSS can support various types of problems be they semi-structured, ill-structured, tame or wicked (Rittel and Webber, 1973, Keen and Scott-Morton 1978, Bonczek, et al. 1981, Sprague and Carlson, 1982). Last, in a DSS the focus in the decision process is on effectiveness versus efficiency.

4.2.5 Wisdom and the Human Factor

The DSS is a tool, which should aid individuals in making the best decision on their own and aid in the creation of collective intelligence when a group is interacting with one another (Hiltz and Turoff, 1978). A decision support system can be a lot of things; however, it cannot replace the human component where judgment needs to be exercised. The function of a human decision maker as a component of DSS is not to enter data to build a database, but to exercise judgment, or intuition, throughout the entire decision making process (Eom, 2001). For both individuals and groups, the DSS should ultimately provide the basis for a group to reach beyond their own knowledge and advocate their judgment in the form of wisdom. “Wisdom goes beyond knowledge because it allows comparisons (judgments) with regard to know-what and know-why. “It is a long way from data to wisdom” (Zeleny, 1987, page 60).

In the remainder of this chapter we will first identify design contingencies for DSS identified as group size, proximity and problem type. Next, the requirements needed by users will be discussed and roles will be defined. Membership rights are then defined and elaborated as to who gets what membership rights according to the role they play. Many types of DSS will be described depending on the nature of the group working together, their problem types and proximity to one another. Studies are investigated concerning group activity coordinators in the form of facilitators and moderators and such, where their effects on the outcomes of the decision making are compared. Last, some methodologies are discussed where particular types of group problems and task types are identified. The chapter ends by drawing together some concerns of existing problems that need improvement.

4.3 Design Contingencies

The objective behind a DSS is to act as an interface for a decision maker, giving them the proper set of tools, based on appropriate models and database systems, all in an effort to assist in making an effective decision (Turoff and Hiltz, 1982). According to DeSanctis and Gallupe (1987), there are three primary contingencies that should be considered when designing a DSS: the size of the group, individual proximity, and the type of task before the group.

4.3.1 Size Matters

Systems are built to support individuals or groups (Turoff and Hiltz, 1982). Decision making groups are defined as “two or more people who are jointly responsible for detecting a problem, elaborating on the nature of the problem, generating possible solutions, evaluating potential solutions, or formulating strategies for implementing solutions. The members of a group may or may not be located in the same physical location, but they are aware of one another an perceive themselves to be a part of the group which is making the decision” (DeSanctis and Gallupe, 1987).

These group sizes can range from a few people co-authoring a paper, to a medium size group of people such as the typical size for a class (30), to very large groups of people interacting globally (White, et al, 2008a). Information overload is a concern for individuals and groups. When large groups work together, tools for filtering or skimming should be implemented (Hiltz and Turoff, 1985).

When working with groups and the consideration of group estimation processes, there are some tautologies which are significant: (Dalkey, 1967)

1. The total amount of information available to a group is at least as great as that available to any member;

2. The median response to a numerical estimate is at least as good as that of one half of the respondents;

3. The amount of misinformation available to the group is at least as great as that available to any member;

4. The number of approaches (or informal models) for arriving at an estimate is at least as great for the group as it is for any one member.

4.3.2 Proximity

Groups of individuals working together can be either be collocated, distributed and not together, or partially distributed where some members are collocated while others are distant.

4.3.2.1 Face-to-Face

Groups who work together in one geographic location are referred to as Face-to-Face (F2F). Huang and Ocker (2006) define distributed as “a collection of geographically, organizationally and/or time dispersed individuals who work interdependently, primarily using information and communication technologies, in order to accomplish a shared task or goal.”

4.3.2.2 Partially Distributed Teams

In partially distributed teams (PDTs), the subgroups are collocated but distant from other subgroups (Huang and Ocker, 2006). PDTs are defined as “a virtual team, in which some sub-groups are collocated, yet the subgroups are dispersed from each other, and communication between them is primarily by electronic media” (Plotnick, et al, 2008). Given the proper set of tools in a computer mediated communication system, groups and subgroups can work as effectively, if not better, using electronic technology (Turoff and Hiltz, 1982).

4.3.2.4 Online Communities

Where most traditional groups have a F2F element, more organizations are moving to an online format where a community forms and utilizes CMC to conduct their daily business. There are many definitions of online communities, three of which are presented here. Hagel and Armstrong (1997) define a virtual community as a computer-mediated space where there is an integration of content and communication with an emphasis on member-generated content. Lin (2003) defines a virtual community as “the gathering of people with common interests to share information and coordinate their work via information technologies, specifically for transaction, interest, fantasy, and relationships.” Preece further specifies an online community as consisting of:

· People, who interact socially as they strive to satisfy their own needs or perform special roles, such as leading or moderating.

· A shared purpose, such as an interest, need, information exchange, or service that provides a reason for the community.

· Policies, in the form of tacit assumptions, rituals, protocols, rules and laws that guide people’s interactions.

· Computer systems, to support and mediate social interaction and facilitate a sense of togetherness.” (Preece 2000, p.10)

Although DeSanctis and Gallupe (1987) strongly believe that the aforementioned contingencies are important, they specifically designated group size and proximity to be the most critical factors upon which to design a group decision support system (GDSS). Figure 4.2 describes the taxonomy they came up with to further describe the different settings for a GDSS.

Figure 4.2 Different Settings for GDSS

The most static of the settings calls for a Decision Room. This is basically a computer/technology enhanced room for a group of collocated decision makers. This is normally a U-shaped meeting room where each member can see the other members, where both individual as well as group support systems are in place. The Legislative Session setting is described as a Decision Room, but much larger. More rules are in place for the discussion facilitation and a moderator may need to be present to ‘rule’ over the procedural process. The Local Area Decision Network consist of a smaller group of people who are geographically dispersed be it down the hall or across the country. The last setting is described as a Computer Mediated Conference. This supports large groups of individuals working together in a distributed mode (DeSanctis and Gallupe (1987).

4.3.3 Problem Types

There are different types of problems that can be handled and supported by a DSS. Due to the basic nature of how humans think and make decisions, these problems are unstructured (Simon, 1960). Problem types for DSS range from semi-structured to wicked (Simon, 1960, Rittel and Webber, 1973, DeSanctis and Gallupe, 1987). It’s the problem type and level of (or lack of) structure which can greatly define the functionality requirements needed by the system (Simon, 1960, Hiltz, et al, 1996, Zigurs, 1998, Eom, 2001). Different types of problems require a different approach.

Some problem types are:

· Structured – this is a problem that has a discrete process in order to solve the problem. This can be programmed as an automated task in an information system (Simon, 1969). There is no need for a complex decision support system as the variables are known beforehand and a simple step-by-step design such as that of an enterprise resource system, can be structured.

· Semi-strucured (DeSanctus and Gallupe).

· Ill-structured (DeSanctus and Gallupe).

· Wicked (Rittel and Webber, 1973) This is not defined as any problem with malice intent, but where the there isn’t clarity or consensus on definitions, goals and such. The facts may be based on a belief system which makes statements neither right nor wrong.

McGrath (1984) integrated numerous task types and categorized them into a ‘circumplex model’ which defined the tasked by what the group had to accomplish during their meeting. There are three tasks identified as 1) the Generation of ideas and actions, 2) Choosing from those alternatives and 3) Negotiation among alternatives.

4.3.3.1 Structured Problems

Structured problems are inflexible and have workable, discrete solutions that can be automated. However, when the environment changes, these automated solutions can be dealt the wrong solutions (Turoff and Hiltz, 1982).

4.3.3.2 Semi-structured Problems

Semi-structured problems are “expressed in terms of problems that can be organized” (Keen and Morton, page 861). Many problems fall under this category and must be dealt with by decision makers within organizations. These groups tend to be dispersed geographically as well as organizationally, and must use systems that support the needs of these members (Turoff and Hiltz, 1982).

4.3.3.3 Ill-structured Problems

Decision makers may have to interact with others in order to better understand the needs of an ill-structured problem (Turoff and Hiltz, 1982). To help groups of decision makers understand a problem and come up with effective solutions, a GDSS needs to use a combination of technologies in order to aid in communication, computing and decision support (DeSanctis and Gallupe, 1987). Larger groups generate a better analysis of a complex problem and have a better chance at turning an ill-structured problem into a semistructured one (Turoff and Hiltz, 1982). Ill-structured problems are defined as and grouped together with ‘messy’ or ‘wicked’ problems (Horn, 2001).

4.3.3.4 Tame Problems

A tame problem is by no means a structured problem, but does have a well-defined problem statement which has a solution that can be objectively evaluated as right or wrong.

By definition, a tame problem has a well-defined and stable problem statement; a definite stopping point; a solution that can be objectively evaluated as being right or wrong; solutions that can be tried and abandoned; and belongs to a class of similar problems that can be solved in a similar manner (Digh, 2000).

4.3.3.4 Wicked Problems

By contrast, wicked problems like affordable housing, disparities in healthcare, and institutional racism are ill-defined, ambiguous and can be associated with strong moral, political and professional issues and are constrained by political, economical, cultural and ideological values (Digh, 2000, Horn 2005). Wicked problems are volatile and of a very dynamic nature with considerable uncertainty and ambiguity (Horn, 2005).

There are many causes of wicked problems which are coupled with other issues and constraints all of which are evolving and unfolding over time (Weber and Rittel, 1973, Horn, 2001). Each wicked problem is unique with no definitive formulation. Wicked problems are not well understood until they are experienced, and only when potential solutions are formulated does one understand the problem more (Conklin and Weil, 1998, Conklin, 2005) The problem is complex and recursive in nature for when you understand one problem, it only leads to questions that lie defining other problems (Horn, 2001). Since wicked problems are unique and have not been experienced before, a well-defined solution set is not feasible nor available (Rittel and Webber, 1973).

Since you cannot define the problem, it is difficult to tell when it is resolved since judgment is based on not right or wrong, but good or bad which is subjective and from the perspective of the stakeholder (Rittel and Webber, 1973, Digh, 2000, Horn, 2001, Conklin, 2005). Formulating the problem and the solution are essentially the same thing. Each attempt at creating a solution changes the understanding of the problem. Every wicked problem can be considered a symptom of another problem. Solutions to wicked problems are not true-or-false but good-or-bad. There is no immediate and no ultimate test of a solution to a wicked problem. Every implemented solution to a wicked problem has consequences. Solutions to wicked problems generate waves of consequences, and it is impossible to know how all of the consequences will eventually play out (Rittel and Weber, 1973).

Wicked problems are ongoing and have no stopping rule (Rittel and Webber, 1973, Digh, 2000). They are never resolved, but change over time (Conklin, 1998). Wicked problems are solved per se when they no longer hold interest to the stakeholders, when resources are depleted or when the political agenda changes (Horst and Webber, 1973). There are many stakeholders with multiple value conflicts who redefine what the problem is repeatedly, reconsider what the causal factors are and have multiple views of how to approach and hopefully deal with the problem (Rittel and Webber, 1973, Conklin, 1998, Digh, 2000). Getting and maintaining agreement amongst the stakeholders is most difficult as each has their own opinion of what is best (Rittel and Webber, 1973). As in ill-structured problems, wicked problems are best managed if they can be turned into a tame or structured problem (Rittel and Webber, 1973, Turoff and Hiltz, 1982).

4.4 Varieties of Decision Support Systems

Decision support systems are designed to meet the needs of the users. Some considerations are based on the number of users and their proximity to one another.

4.4.1 Computer Mediated Communication Systems (CMC)

Computer Mediated Communication systems is a general description of computer systems being used between people in a variety of ways such as e-mail, web pages, forums and such. They can be used synchronously, asynchronously and in face-to-face meetings or in a distance format (DeSanctis and Gallupe, 1985, Hiltz, et al, 1996).

4.4.2 Single User Decision Support Systems

When a single user is using a system for decision support, the system is used for information gathering and inquiry seeking needs (Churchman, 1971). Information is presented in graphs and tables or any other means of filtering information into a manageable amount so as to not suffer from information overload (Hiltz and Turoff, 1985). Software can act as a part of the solution to information overload. The individual must learn to use the system by selecting their own criteria filtering information and to develop the social skills that will be necessary to work effectively with other group members (Hiltz and Turoff, 1985).

As the workforce is increasingly working on a global level with other co-workers, a trend has developed to not only use DSS for single users decision making, but to use them to network organization members together into distributed decision making (DDM) systems (Eom, 2001). These single users are connected in such a way as to be able to work independently of one another on a sequential joint production (Hiltz and Turoff, 1985, Rathwell and Burns, 1985).

4.4.3 Group Decision Support Systems (GDSS)

A Group Decision Support System is defined as 'an interactive computer-based system which facilitates solution of unstructured problems by a set of decision makers working together as a group' (DeSanctis and Gallupe, 1985, pg. 3). Group Decision Support Systems (GDSS) are characterized as “a set of software, hardware, and language components and procedures that support a group of people engaged in a decision-related meeting” (Huber, 1984). The proper combination of tools or structures can help a group be more efficient and get better outcomes or make better decisions (Turoff and Hiltz, 1982, DeSanctis and Gallupe, 1985). There is a greater chance for equal participation and numerous ways to interpret data such that the human will understand the information better (Hiltz, 1996).

One integrated system should be used for group software such that the individuals use one set of technologies (Turoff, et al, 1993). Group support systems have a primary goal of helping groups be more productive (Gert-Jan de Vreede, 2001). Group support systems offer a wide variety of tools from which both individuals and groups can use to make better decisions, structure activities and improving communications by supplying the proper technology (Huber, 1984, DeSanctis and Gallupe, 1985, Hiltz, 1996, Gert-Jan de Vreede, 2001). Especially when given large groups of individuals working together, the end result is a collective intelligence, where the group is smarter than any one individual (Hiltz and Turoff, 1985, Turoff, et al, 2002).

4.4.3.1 Levels of GDSS

Where it concerns members exchanging information, DeSanctus and Gallupe (1987) defined three Levels/approaches for group support. Level 1 concerns communication mediums. It focuses on removing communication barriers and facilitating the information that is exchanged between group members. Level 2 encompasses the incorporation of modeling technologies into the interpretation of data and also methodologies to aid in the reduction of ‘noise’, decreasing information overload and aiding in decision making where it involves uncertainty. Level 3 includes supporting more intelligent, dynamic decision making where rules enforcing patterns of information exchange are imposed by expert advisors during the meeting. For example, Roberts Rules of Order could be implemented in a Level 3 GDSS where the system could even participate in the building or selecting of rules in the decision making process.

4.4.4 Distributed Group Support System

"Distributed Group Support Systems" embed GDSS type tools and procedures within a Computer-Mediated Communication (CMC) system to support collaborative work among dispersed groups of people. "Distributed" has several dimensions: temporal, spatial and technological (Hiltz, et al, 1996).

Communication support can be provided by a DGSS 5 different ways (Turoff, et al, 1993). One way is by offering numerous alternative communication channels so as to support different types of information be it textually based, graphically based or structured data. Second, rules and protocols can be implemented along with user roles so that structured interactions along with membership permissions can enforce rules to improve the groups performance. Third, a GDSS should aid both the individual and the group with information seeking while reducing information overload through a variety of functionalities such as information gathering, filtering, feedback, etc. Sophisticated decision making methods are a fourth way that these systems can support the communication needs. And last, but not least by any means, is that a DGSS can help synchronize the entire communication process where it concerns any phase in decision making. Last, Turoff mentions that a group memory should be utilized for future use and/or to help in any other phase (Turoff, et al, 1993).

4.4.5 Social Decision Support Systems (SDSS)

The primary objective of a social decision support system is to facilitate the gathering of multiple points of view from varying perspectives and creates a working body of knowledge.

(Turoff, et al, 2002)

SDSSs are designed given a specific set of governing criteria and objectives (Turoff, et al, 2002). Structures are based on the issues which need to be discussed, the options that are available from which to choose, the comments that the individual group members may wish to contribute and the relationships between the three (Turoff, et al, 2002).

4.5 Information Overload

If there is too much or too little of a workload, human performance deteriorates (Rouse, 1975, Hiltz and Turoff, 1985). Information overload is defined as “information presented at a rate too fast for a person to process” (Sheridan and Ferrell, 1974). Computer mediated systems should provide the necessary tools to help users filter and organize data, helping them to screen large amounts of incoming information (Hiltz and Turoff, 1985). Novice users will have to put the time into learning a system as experienced users will manage their information better since they already have devised ways of filtering (Hiltz and Turoff, 1985). There are ways to overcome information overload using a computer. First, human roles can implement functionalities into particular categories of membership thus restricting the information down to a minimal. Second, protocols can be implemented directing the group what to do when and third, knowledge structures can be used to aid in decision making (Turoff, et al, 1991).

4.5.1 How It Happens

There is a correlation found between user experience with a system and management skills reducing information overload (Hiltz and Turoff, 1985). Studies conducted with users on management information systems show that intermediate users suffer the most from information overload (Dickson, et al, 1977).

4.5.2 What Occurs From It

There are many negative ways that humans deal with information overload which are conducive for poor decision making. Hiltz and Turoff offer a list (1985, page 682) of ways individuals may cope:

1. Fail to respond to certain inputs,

2. Respond less accurately than they would otherwise,

3. Respond incorrectly,

4. Store inputs and then respond to them as time permitted,

5. Systematically ignore (i.e., filter) some features of the input,

6. Recode the inputs in a more compact or effective form, or

7. Quit (in extreme cases) (Sheridan and Ferrell, 1974).

The primary way individuals cope with too much information is by filtering and omitting or ignoring it (Miller, 1962).

4.5.3 How to Decrease It

There are a number of ways to decrease information overload. An example is that communications can be regulated by social pressures. Also, user experience in learning to use a system efficiently with the given tools and having numerous options for personal preference in the filtering of information is required in the design of a system (Hiltz and Turoff, 1985). Information overload has been discussed throughout this SOTA and should be referenced for other ways already indicated on how to reduce information overload.

4.5.3.1 Data Management

There are two basic processes for increasing the organizational efficiency of information systems: message routing and message summarizing (Huber, 1982). Also, system notifications of information having been read already or on the other hand, that new information has arrived, aid in the reduction. More communication options giving the end user the freedom to filter and skim also greatly reduces the incoming data into a manageable set of information (Hiltz and Turoff, 1985).

4.5.3.2 Social Influence

There are social pressures that can aid in the reduction of information (Hiltz and Turoff, 1985). One, restrictions or limitations can be placed on the length of comments allowed. Two, allow the group to create their own norms as the membership increases or decreases to reflect the views of the group (Hiltz and Turoff, 1985). Pen Names and anonymity along with individual member rights to view, post or edit information can also reduce information overload (Hiltz and Turoff, 1985)

4.5.3.3 Conferencing Systems

Conference systems can aid in the management of large amounts of data. A conference system allows for discussions to occur. This system along with a message system can greatly reduce the flow of unwanted messages (Hiltz and Turoff, 1985).

4.5.3.4 System Induced Measures

There are automated methods that can be employed in a system such as ‘self-destruct’ dates to aid in eliminating unwanted data which in turn reduces the information to sift through (Hiltz and Turoff, 1985). Systems can aid in data management which reduces information overload by implementing database methods such to help sort, search and find strings of information in textual based documentation (Hiltz and Turoff, 1985).

4.6 Process Catalyst

A person or group of persons can fill or implement process catalyst roles using the computer mediated communication system. These catalyst are referred to as a facilitator, coordinator, moderator, leader, chauffer or user driven system (Hiltz, Johnson and Turoff, 1982, Hiltz, 1984, Hiltz and Turoff, 1985, DeSanctis and Gallupe, 1987, Bostrom et al., 1993, Hiltz, 1996, Limayen, 2006). Some of these roles or parts of the catalyst functionalities can be automated (Gert-Jan de Vreede, 2001, Limayen, 2006),

These aforementioned roles can edit, move, delete, or prompt (amongst other functionalities) discussions, trigger voting among particular subgroups, or request actions taking on the powers defined by the role (Hiltz and Turoff, 1985, White, et al, 2007a).

4.6.1 Leader

Leadership and process are key variables that influential in decision making amongst a small group of individuals in a traditional face-to-face environment (Hiltz, 1996). Processes can help in rational decision making and in brainstorming (Osborn 1957) or Nominal Group Techniques (Van de Ven & Delbecq, 1971), can enhance process and outcomes. These same variables aren’t as effective in an asynchronous distributed environment (Hiltz, 1996). Using the group as the decision-maker proves better than any single individual (Condorcet, 1976, Hiltz and Turoff, 1993)

4.6.2 The Facilitator

Human facilitation is where an individual intervenes using special privileges provided by the role in the software. This individual aids in the group’s decision making by carrying out a set of activities during the duration (before, during and after) of the process (Bostrom et al., 1993, Limayen, 2006). Human facilitators can be key in improving group performance (Reagan-Cirincione, 1994) and can help groups accomplish their task (Hiltz, Johnson and Turoff, 1982). Conference leaders are called coordinators and prove useful in enforcing protocols to aid in the navigation (Hiltz, Johnson and Turoff, 1982). Groups may need help in using a system and the facilitator can be used as an interface (DeSanctis and Gallupe, 1987). However, this technical driver is also referred to as a chauffeur (Dickson, 1989).

Whether to have a designated facilitator, human or automated, is a dicey question in a community using a computer mediated communication system. Whitworth (1999) describes the interaction method which requires the need for a facilitator to motivate the people during the entire decision making process. Other types of group interaction such as Robert’s Rules of Order require a monitor or parliamentarian to keep the meeting on track and enforce the rules (Pitt, 2005). Hiltz and Turoff, 1996 believe that, no matter the complexity or organization, there will exist a need for a facilitator or moderator. They state that in particular given a Delphi system, human intervention is barely required due to the structure of the methodology. If someone is used, the software powers or special privileges that such an individual needs are:

· Being able to freeze a given list when it is felt there are sufficient entries to halt contributions, so as to focus energies on evaluation of the items entered to that point in time.

· Being able to edit entries to eliminate or minimize duplications of resolutions or arguments.

· Being able to call for final voting on a given item or set of items.

· Being able to modify linkages between items when appropriate.

· Reviewing data on participation so as to encourage participation via private messages.

While a lot of material in an online Delphi can be delivered directly to the group, the specific decisions on this still need to be made by the person or team in control of the Delphi process. In Computer Mediated Communications, the activity level and actions of a conference moderator can be quite critical to the success of an asynchronous conference and specific guidelines for moderators can be found in the literature (Hiltz, 1984).

Groups, especially in the virtual setting, need a facilitator to introduce agendas or trigger a decision making process (Tarmizi, 2006). A facilitator is a key role in building a online communities. ‘One of the techniques that can help to sustain a community of practice (CoP) is the introduction of a facilitator, since a facilitator can play a crucial role in addressing the challenges of establishing and nurturing a community (Tarmizi, 2006). One of the most important aspects of the policies and procedures that govern an online community is the set of roles and responsibilities that are used (Fontaine, 2001). In particular, leadership in the form of facilitators or moderators is crucial. Another crucial aspect is the norms and practices of interaction that support trust and build community spirit and cohesiveness.

4.6.3 Moderator

Problems can arise when group consensus is unattainable and a moderator may need to intervene. A facilitator can now change to a moderator as long as it’s a 3rd party non-bias member of the group who is there to help navigate the group towards a satisfactory consensus (White, Hiltz and Turoff, 2007). Another important part of this is presented by Voss (2003) where he states “for an e-discourse it is important to be documented at a shared place so that every one can access the same version at any time from anywhere.”

Over time Cristal (2006) notes that once the community members are interacting actively, less organizational intervention can occur for the group (community) will now exist on its own devices.

4.6.3.1 Motivation

Catalysts can be used as motivators. One of the largest barriers to get over in creating online communities is getting the members to participate in the initial stages of its creation. Members have a higher satisfaction rate when they collaborate as a community (Kimble, Li, and Barlow, 2000). Exposing the interest and defining these interest play an important part behind motivating individuals to come to the site initially, then participate eventually. It’s the domain of information which should be one of the driving forces behind the motivation to participate and then, to offer something to the members that they cannot find elsewhere (Wenger, 2003). “A cooperative relationship among members as a way to increase sense of community, which in turn could lead to increases in participation” (Tarmizi, 2006). It’s the community itself that must discover and be the driving force behind this motivation.

Anonymity can be taken too far (Turoff and Hiltz, 1996). It is important that experts know that they are working with other credible people to make it worth their effort. This is a motivating factor in motivating participants to interact. Anonymity can still be put in place and at the same time, users know the group of people they are working with. For example, there could be a list of people’s name and affiliations given so that the user knows they are working with this identified group of people, but still have them use pen names and such. Also, people can have the ability to identify themselves if they so choose. Last, anonymity can be used selectively as can the identification of group participants. For example, the experts may want to use their real names during the arguments, detailing where the information for the argument comes from, but then have the voting anonymous.

4.6.4 Chauffer

A “chauffeur” is described as a person who helps groups through the technical aspects of a system. Dickson et al. (1989) propose that this is beneficial if a group is using a system very rarely. The term chauffeur is defined as a non-bias individual external to the decision making of the group and simply ‘drives’ the group through the process (Dickson, et al., 1989).

4.6.5 Automated

As stated earlier in this chapter, structured problems can be automated. However, even dynamic structures can have automated functionalities that can, for example, reflect a vote (White, et al., 2007a). Automated facilitation can implement protocol into a system executing a structured decision making process (Limayem and DeSanctis , 2000, Limayen, 2006). Based on human thinking models, automated facilitators aid the group in reaching their outcomes by providing the group guidance during decision making (Limayen, 2006). Also, the automation of some facilitation task have proven to have positive effects (Gert-Jan de Vreede, 2001).

4.7 Methodologies

Decision making can be made by individuals or groups. The decision making can have a single dimension or be multidimensional (Bard and Sousk, 1990,

Iz and Gardiner, 2005). Many issues can be measured during decision making such as decision outputs, changes in the decision process, changes in managers' concepts of the decision situation, procedural changes, cost/benefit analysis, service measures and managers' assessment of the system's value (Keen and Scott Morton 1978, Eom, 2001).

4.7.1 Unidimensional Data

When working with unidimensional data, items can be ranked from one area to another opposing area such as what occurs in ranked data (Thurstone, 1927a). Paired comparisons have been used a number of ways to support both individual and group decision making (Thurstone, 1927a, 1927b). They have also been used for the formation of weight assignment, preference scores (Saaty, 1980, Thurstone, 1927a, Ichikawa, 1980, Kok, 1986, Kok and Lootsma, 1985). Paired comparisons can assign weights to unidimensional data or it can give comparisons on each item unit as each stands relative against one another (Thurstone, 1927b, Iz and Gardiner, 2005, White, et al, 2008b). Paired comparisons are good to use with larger sets of data because they give a more accurate reflection of a user’s opinion than if the user were to rank each item by eye (Miranda, 2001).

It’s suggested to use AHP with results from paired comparisons for cooperative groups (Saaty, 1988, Iz and Gardiner, 2005). AHP used paired comparisons along with the arithmetic mean to calculate weights and preference scores or can be used to place alternatives into a cardinal ranking (Saaty, 1988, Bard and Sousk, 1990, Iz and Gardiner, 2005).

4.7.2 Multicriteria

Multiple criteria decision making (MCDM) has numerous options with differing weights, and various stake holders with subjective needs. There are numerous techniques that use multiple criteria in their decision making technique (Iz and Gardiner, 2005). One of the more popular methods used is Multi-Attribute Utility Theory (MAUT) (Nakayama, 1979, Iz and Gardiner, 2005). Another technique is Multiobjective Mathematical Programming (MMP) which deals with complex problems processing numerous decision variables that have constraints (Mavrotas, 2008). AHP is also used in MCDM as a way to rank order alternatives (Saaty, 1988).

4.7.3 Measuring Decision Support

There are numerous ways DeSanctis and Gallupe (1987) offer to measure decision support. One way would be to measure the outcomes of the meetings against a set of predetermined goals and objectives or even measuring the outcome of what was thought to occur versus what has occurred. Another way to measure would be to survey the users and find out how satisfied they were with the decision making that took place or whether the group members are willing to work together again (DeSanctis and Gallupe, 1987).

4.8 Conclusion

We have demonstrated that there are many issues to take into consideration when designing a decision support system. Group size, task type and proximity are major considerations however; we also note how communication structures must be provided to support the particular needs of the group. Problem types need to have their approach catered to their environmental needs and some processes need some form of catalyst for discussions. Some issues that are of particular interest are:

· How to best create a system for a very large group of users;

· How to manage the roles these users fill and the privileges that will be given to them on the system;

· How to best manage the increasing mass of information presented to the user;

· Which model best reflects the group’s opinion;

· Wow to best implement human or automated facilitative roles;

· How to best present the information to the user for fast and accurate decision making needs.

Therefore, we believe as a critical part of this research endeavor, the system must be tailored to support the communication needs of the users.

CHAPTER 5

VOTING

If you choose not to decide, you still have made a choice.

Freewill -Rush

Abstract

The topic to be covered in this section is about voting and how it can be used for group decision making. The objective of this section is to show how voting can be used in this effort, demonstrating the pros and cons of voting while outlining the areas where it can be used best. Also, I will show how different voting methods can be used in different parts of the decision making process fulfilling the needs of the users with a multitude of voting methodologies. For example, there isn’t only one type of voting method implemented, but rather, a multitude of methods can be used together synchronously or asynchronously in order to cater to more complex environments. Past research has demonstrated voting as a productive tool which reflects the group’s opinion concerning an issue. This paper concludes demonstrating the need for the proper type of voting to best fit the task given where the population’s preference is based on an agreed upon voting method.

5.2 Introduction

Defined by Robertson, (2005), “Voting in its broadest context is considered to be an ongoing activity that involves information gathering, attitude development, opinion management, collaboration, periodic consideration of specific candidates and issues, decision making in elections, and tracking.” Obviously, all of these areas defined can be supported by technology although maybe not interpreted due to the inner complexities of the formation of the mind’s opinions, beliefs and interests, but nonetheless, supported. Hiltz and Turoff further define how votes which lie on the outer limits of the group means can be a useful tool in understanding the reasoning behind individual voter’s opinions (Hiltz and Turoff, 1978).

Voting can be used as a unified visual interpretation to guide groups in decision making. There are many different voting methods upon which various criteria further define the outcome of the vote. Different methods can be used, for the different needs a group may encounter during ongoing decision making process cycles. Voting is used for both rational and social decision making (Whitworth, 1999). There are numerous complexities within the users which may influence the direction in which people vote (Robertson, 2005).

Another dynamic posed by voting is that ‘when’ a group votes has implications in itself (Whitworth, 1999). Voting has been used in social decision making and has proven useful as a tool to reflect a group opinion (Turoff, et al, 2002; Li, 2003, White, et al, 2007b) and has also been demonstrated to work on complex problems lacking structure (Whitworth, 1999). However, voting has not reached the potential and recognition that it could have as an integrated part of group decision making through input, output, feedback and as a stimulus for discussion which aids in reducing many negative side effects.

I will briefly explain how voting could be used to support the following electoral processes identified by Robertson, 2005:

· Information gathering. Social book marking systems are one way in which people can use voting as a tool in identifying useful information.

· Deliberation. Using voting as a visual feedback mechanism which further stimulates discussions where areas of disagreement exist.

· Decision making. Choosing from a list or ranking a list in an order is at the heart of voting and is what it is used for initially.

· Evaluation. Reflections of decisions made are nothing more but more lists built upon some set of criteria, in this case, as a tool for feedback on some given outcome.

Voting can give members a range of values from which to choose. This can affect how the end results are tallied. Furthermore, these calculations need to reflect and satisfy the individuals as well as groups in their decision making. Also, the list may seem to reflect no more than a Likert scale, however, other alternatives are offered that are not used in such a scale at all. Although this is not an exhaustive list, some selections are presented demonstrating the range of feedback that can be considered to best reflect a voter’s preference (Pitt, et al, 2005; Turoff, et al, 2007):

1. Strongly Agree;

2. Agree;

3. Slightly Agree;

4. In the Middle;

5. Slightly Disagree;

6. Disagree;

7. Strongly Disagree;

8. Abstain, No Vote;

9. Don’t Understand;

10. Waiting for More Information;

11. Not Being Knowledgeable;

12. Would only vote for if this were the only choice available;

13. Would not vote for under any circumstance[2].

It’s the other selections offered which can aid a group in making the best decision as a multitude of information gathering instruments can aid users. These tools can help by covering the spectrum from uncertainty due to a lack of information to information overload (Hiltz and Turoff, 1978, Li, 2003). Other alternatives should be offered to best reflect the expert’s state of mind. For example, “it should be clear to the respondents that they do not have to respond to every question, but can decide to take a "no judgment" view” (Turoff and Hiltz, 1996, pg 4).

In this section, voting methods will be described and fairness criteria will be explored. Next, I will discuss the conditions of Arrow’s Impossibility Theorem. Voting protocols will be discussed and Roberts Rules of Order will be used as an example. Voter’s Roles will be defined and the powers one holds using a vote as well as how they are allowed to apply a vote will be discussed. Next, the benefits or problems associated with discussions will be explored describing how voting can have a direct effect on them either way. Problems with bias in voting will be explored and ways to reduce it will be presented. Decision making using voting methods under uncertainty will be explored. Finally, anonymity and how it changes multiple attributes of voting will be presented.

5.3 Voting Methods

There are quiet a few voting methods upon which a few shall be mentioned here. Different types of voting are used all around society in everything from elections to choosing talent. These can include both very small groups considering simple situations and on the other extreme, very large groups addressing very complex problems. The ways in which the end results are tallied vary using one method versus another can end with drastically different outcomes. Other issues include votes can be weighted and how this can greatly influence the outcome.

5.3.1 Plurality Method

The first method presented is the Plurality method. This is a simple method where the end result is calculated by having the highest number of first place votes. This is a method where items are ranked from first to last.

5.3.2 Plurality with Elimination

An extension of this method is Plurality with Elimination. This is a voting method consisting of rounds whereby each cycle eliminates the item which scored the lowest. This rotation continues until there are only two remaining items from which to choose and one will be selected based on a majority criterion. However, this majority could be further broken down where half of the votes come from one source and where the other half comes from another source.

5.4.3 Borda Count Method

The Borda Count Method is also used for ranking items or selecting a single item. This is used as a surveying method where every position from least to most is given a corresponding number which complements the understanding of the scale, i.e. intervals. So, every item selected is given a weight and then these weights are calculated in order to give a group rank.

5.4.4 Pairwise Comparisons

Pair-wise Comparisons is another method upon which group judgments can be made. There are numerous variations on this method upon which tallies can be made. One utilized in this research effort is from Thurstone’s Law of Comparative Judgment (Thurstone, 1927). All items in the set are matched against all other items in the set on a pair-wise basis where one item is selected over the other item based on some unidimensional criteria. However, ties can be treated differently. In particular, Thurstone’s Law dictates that there will be no ties allowed. Other methods split the value in half giving each item half the weight. So, the selected item in the pair gets a point, and if a tie should occur, then both items receive half a point. The items are summed giving a ratio where each pair equals 100%. Next, these proportions are given a corresponding number from a Unit Normal Table. The items are then summed and a final ranking is produced.

5.4 Fairness Criterion

Voting can be used to reflect a group’s opinion. There are numerous types of voting and criteria from which they are judged. Some criterion upon which voting is deemed fair are outlined under the Majority Criterion, the Condorcet Criterion, the Monotonicity Criterion, and the Independence of Irrelevant Alternatives Criterion. These are situations in which there are specific rules to implement such that the listed criterion remains true to its definition. The difference between these four mentioned is, if and how any rounds may be implemented, and how the calculations are made.

5.4.1 The Majority Criterion

The Majority Criterion states that, given a set of candidates, whoever gets the most votes should win. In one single voting procedure, this would be determined. It matters not the difference between candidates, if there’s one vote or a million, the end result is the same.

5.4.2 Condorcet’s Criterion

Condorcet’s Criterion is a bit different. This states that for every ordered pair where any given set {x,y} = {y,x}, whoever wins the most paired comparisons, given that every pair is compared, wins. These varied procedural variations give different outcomes. For example, given the same set of items and choices made by a group of experts, a Majority vote could possibly have winner A and Condorcet winner B.

5.4.3 Monotonicity

Another criterion is the Monotonicity criterion. This states that once a vote takes place, no change in merit will disrupt the end result previously calculated, through a revote. It states that once a vote goes to an item, the vote must remain with that item such that if an item is selected as the top choice, a revote should provide the same outcome.

5.4.4 Independence of Irrelevant Alternatives

Last, the Independence of Irrelevant Alternatives criterion is covered. This states that, if the number of items upon which a decision is selected that gave a particular outcome is modified, this shift in number should not affect the outcome.

5.5 Arrow’s Impossibility Theorem

A system of social choice or better known as voting, takes collective individual preference orderings into a group preference ordering. Arrow’s argument was that a reliable, truly representative voting system should possess certain desirable properties all of which can never be satisfied simultaneously. The axioms include: 1) Universal Domain, Completeness and Transitivity, 3) Positive Association, 4) Independence of Irrelevant Alternatives, 5) Non-Imposition and 6) Non-dictatorship (Tabarrok, 2005). Since all of the aforementioned cannot be satisfied simultaneously, something must be sacrificed. Which, and are there different circumstanced that can maintain a robust system?

Arrow defined three areas in which restrictions should be made during voting: 1) the input, 2) the output and 3) how the final calculation is made. Arrow’s theorem basically states that there can never be a consistent outcome when more than 2 items are being considered in a vote. Further he states that there cannot be a group decision determined from individual input (Arrow, 1951). “Furthermore, standard averaging approaches can lead to inconsistencies in group judgments” (Hiltz and Turoff, 1996, pg. 13).

The axioms which place restrictions on inputs are:

1. Universal Domain: Input to the voting system by all individually rational preference orderings are allowed. One cannot assume that all individuals have the same exact preference. If so, then there would be unanimous consensus and no need for a voting system.

2. Completeness and Transitivity: There must be consistency for any given input to have a reliable output.

a. Without completeness, the voting system fails and doesn’t answer our questions given the vote and an outcome.

b. Without transitivity, the voting system fails the by answering our questions ambiguously. Transitivity states that output given the same set of input must be consistent. Checks should be in place notifying individuals of inconsistencies in their choices. An example is provided by Hill where he defines a ‘circular triad’ (Hill, 1953). This is characterized as an error. This is a situation where an individual prefers X to Y, Y to Z, but then Z to X. The output of the voting system must be helpful and indicate some logical meaning or message informing such an error to the end user.

The following are defined by Arrow as axioms which restrict the ways in which individual preferences are transformed into social preferences.

3. Positive Association: For any given vote where X is preferred to Y and then X is raised higher in preference, the same orders of preference given X and Y, should remain the same. Hence, even if X is raised higher in preference, it should remain preferred to Y in order. Positive association states that the social ranking of X to Y should remain in place if no other individuals have reduced their ranking of X.

4. Independence of Irrelevant Alternatives: If X is preferred to Y, then any other alternative selection should not have any effect on the two items X and Y. They should remain in their order of preference. Although other alternatives may be distributed throughout the item list intermittently between the two, any one item which is preferred to the other should remain the same way all things considered. This was identified earlier as a circular triad (Hill, 1953).

5. Non-Imposition: The voter preferences should be reflected in the outcome. This would mean that the end result of a rigged outcome, where an election has been falsified by malicious means, would be non-valid.

6. Non-Dictatorship: The vote should be made by more than one person. For example, if someone declares themselves President and other stakeholders don’t get to vote, it’s not valid.

Although the latter two axioms seem a bit trivial, they still must be identified and written for there’s always someone lurking in the background waiting to manipulate a situation given a social vote. Many nations have mass killings and riots due to axiom 5. How can you know that a vote is fair and represents the true beliefs of a population? Many checks and balances must be put in place. This can be crucial, for a vote can have an end result of great power for any given individual or group of individuals.

5.6 Protocol

Protocol is simply the order in which something is supposed to occur. This can have great effects on how a vote is determined given any complexities or given any changes that may arise. Sometimes strict protocols or rules of order are given in order to properly recognize individuals and to give the voting process structure. When in face-to-face (F2F) meetings, this can be a necessity. However, as a group’s size increases, the need for structure is considered necessary. Although enforcement is prominent in online communities, these protocol structures can be somewhat relaxed although they may still have to be followed. For example, Robert’s Rules of Order are a common protocol for meetings. It is structured such that members can be heard fairly and equally and issues can be discussed in a timely manner. This is primarily to keep group members on a schedule given that they only have so much time in which to conduct the meeting and carry out the items on an agenda.

5.6.1 Robert’s Rules of Order

Robert’s Rules of Order, newly revised (RONR) are a popular set of rules for group meetings. Both large and small groups quite commonly use them. Pitt outlines the formalities of conducting a vote according to RONR as follows:

· “A member proposes a motion

· Another member seconds the motion

· The members debate the motion

· The chair calls for those in favour to cast their vote

· The chair calls for those against the motion to cast their vote

· The motion is carried, or not, according to the standing rules of the assembly” (Pitt, 2005).

It is important to remember that given the nature of group meetings and their asynchronous activities, many of the restrictions and formalities of RONR are not required because of time allowance and geographic proximity. The reasons for implementing RORN are different in online settings. For example, the group may be allowed to interject any thoughts or ideas into any part of a system. This can be accomplished any time and from any where which greatly frees one up from the restrictions imposed in a F2F meeting. For everyone to have the opportunity to be heard is greatly strengthened in an online format with such dynamics (Turoff, et al, 2002, White, et al, 2007b)

5.7 Roles

Roles are extremely important given a set of strict protocol in voting as in RONR. Roles identify what needs should be supported for a given user in a voting system. However, this tends to give people unfair levels of power. This can be seen anywhere from someone’s vote counting more than another’s, to allowing some roles to have input where others may not. However, it’s these sorts of restrictive criteria that help mimic the decision making process the group of voters want reflected. And roles can give structure to a more complicated social interactive system. There is also the need to give members concurrent roles to cover the various needs a user may have, even within a single group that should be taken into account. “It is also becoming quiet evident from existing systems that individuals need multiple identities for the multiple roles they may play, even within a single group” (Hiltz and Turoff, 1978, pp 335). Voters can be identified or anonymous. For example, on one hand, a voter may want to be anonymous during a heated debate. On the other hand, they may want their opinion to be known amongst the population, to have their say. Again, these are issues that are contingent on the meeting format, be it F2F or conducted online, as well as the particular situation and environment in which the issue is conducted.

Pitt describes how the roles may be identified in an agent based community where the various powers given to members are identified as:

1. “Voter: Those agents who are empowered to vote.

2. Proposer: Those agents who are empowered to propose motions.

3. Seconder: Those agents who are empowered to second motions.

4. Chair: Those agents who are qualified to conduct the procedure; one of whom at any time, will be designated to be actual chair, and thereby empowered to conduct the procedure.

5. Monitor: Those members who are to be informed of the actions of others, in particular the votes cast and the decisions reached (Pitt, 2005).”

This last role of monitor takes on various roles when presented with an online community. This particular area can have various meaning in an online setting and is further described in the following chapter. Turoff, et al, in their description of a Social Decision Support System (SDSS), presents a collegial type of role where most voters have the same definitions. This indicated that more ideas and better ideas could be generated from a group of experts (2003).

5.8 When to Vote

A question one may consider is, when should the vote be given? Should it be given once, twice or should it be open? How about the results, when should people be allowed to see the tallies?: at the beginning, end, or should there be a running tally? Hiltz and Turoff offer some reasons for not giving results too early during a voting session:

“Since it may be desirable to hold certain types of contributions until the group is at a point in the deliberations where they are ready to deal with them. Also, information such as voting results should not be provided until a sufficient number of votes about an item have been accumulated.” (Turoff and Hiltz, 1996, p7)

In most group support systems, voting is used as an end to the means for selections or processes (Whitworth, et al, 1999). Historically, political election results are withheld until a designated time. Some Delphi processes have the group members vote at the beginning which is called, round 1, for an initial opinion, then a few more times during the forecasting process (Dalkey, 1967). Other Delphi processes use voting to identify areas of agreement/disagreement (Turoff and Hiltz, 1996). When a group votes and how they vote are issues that should be further explored in general. A few ideas are presented here to demonstrate how unique voting methodologies can be created and used for particular kinds of group needs and consequences.

Research shows that an initial vote on an issue may be a good thing as if there is strong group consensus. This saves time by not proceeding to other steps that may be part of the protocol in order to finish the formality. Other processes from the decision model may be skipped and the next challenge can be encountered more quickly (Hiltz and Turoff, 1978; Whitworth, et al 1999).

Whitworth, et al proposed that a voting before discussing (VBD) method was best when working with groups who have a high level of conflict. That basically, discussion would only be used as a tool for antagonization amongst group members and an avenue that was actually conducive to decreasing consensus. It is the conflict ridden group that can benefit most using VBD. This is because it reduces the chance of members holding further discussion where areas of agreement exist (Whitworth et al, 1999). This idea was brought to light using Delphi processes by Dalkey in 1967 where he states, “The second presumption was that the effect of the undesirable social interactions could be meliorated by imposing a specific format for the discussions” (p 6). Saving time is another benefit of testing for group consensus early. The areas of agreement are bypassed and the group can focus on their disagreements (Turoff, et al, 2002). Voting can be used as a visual feedback system to prompt further discussion on areas of disagreement (Linstone and Turoff, 1975). This can be stimulus for further asynchronous discussion in a virtual setting given real time CMC systems that have access to the Internet (White, et al, 2007b).

5.9 Bias in Voting

Bias in voting can exist in a number of forms. Bias is a large complexity in humans as it resides in many forms, some of which we can remove in a system and some we cannot. Robertson presents a model demonstrating factors which influence our opinions.