Research
Innovation happens when people share their knowledge, combine it in novel ways, change their perspective, and learn from experimentation - in short, through cognitive processes. Our work creates tools and models that aid human cognition so that individuals, teams, and communities can solve "wicked" problems. These problems do not have known answers, are dynamic, and involve people with different perspectives and goals. We use a variety of qualitative and quantitative research approaches, including Fuzzy Cognitive Mapping (FCM). FCM allows us to collect, research, and modify the mental models of experts and laypeople.
What are Fuzzy Cognitive Maps?
Fuzzy Cognitive Maps (FCM) are visual and quantitative representations ("models") of complex systems. They can be used to simulate future system states to answer "What if ?" questions, such as What happens to customer satisfaction if we change product feature X or What might the future business environment be like if trends x, y, z occur in combination? We are the first research group to apply FCM in Technology Management.
In some of our work, we use MentalModeler, a free software developed by our colleague Steven Gray The tool provides a quick and hands-on introduction into FCM and is a great way to get started. We develop our own tools for more specialized analyses.
We regularly organize meetings and workshops on FCM and are always interested in growing the community of FCM researchers. Here are some examples:
Tutorial at University of Missouri, Kansas City (2019)
Tutorial at Hawaiian International Conference on System Sciences (2017)
Portland Summer Workshop on FCM (2016) - some videos
Funded Research
Ongoing
Bringing High-Reliability Safety Culture Decisions into Focus: Training with Interactive Fuzzy Cognitive Mapping, National Academies of Sciences, Engineering, and Medicine: Gulf Research Program ($684,054)
Project Director: Antonie Jetter, Project Team: Wesley Williams, Nan Liang (Louisiana State University), Jennifer Dimoff (Portland State University)
Several studies have called for offshore oil and gas workers to adopt best practices from other high-risk industries, including the nuclear power plant and air traffic control industries. However, frontline managers remain unaware of these external best practices, or have trouble customizing them for offshore oil and gas operations. Inspired by so-called “management flight simulators,” this project creates an interactive online platform that allows users to model responses to everyday safety threats. The platform, FOCOS (Fuzzy Operational Cognition of Safety Culture), lets users add, intensify, or stop interventions, and see how their decisions impact the overall system and safety culture. To inform future research and pilot programs, FOCOS will also collect data on uncertain and controversial safety practices and differences in training needs among different users (by role, professional background, and years of experience).
Recently Completed
Collaborative modeling with fuzzy cognitive maps: A novel approach to achieving safety culture, National Academies of Sciences, Engineering, and Medicine: Gulf Research Program ($407,000)
Project Director: Antonie Jetter, Project Team: Steven Gray (Michigan State University), Steven Scyphers (Northeastern University), Timothy Vogus (Vanderbilt University)
Researchers plan to develop and test a scenario-planning toolkit that oil and gas industry stakeholders can use to explore the factors that strengthen or detract from their organization’s safety culture. They will consider how these factors can be modeled collaboratively, whether modeling can address uncertainty about these factors and their causal relationships, if this exercise helps participants understand what bolsters and hinders safety culture, and whether their participation results in actionable outcomes. Researchers hope this project will produce a modeling approach that organizations can use to develop context-specific safety culture training that is tailored to their unique needs.
Policy scenarios for fire-adapted communities: understanding stakeholder risk-perceptions with FCM, Joint Firescience Program ($181,093),
Principle Investigator; Antonie Jetter, Co-PIs: Lisa M. Ellsworth, Oregon State University, Steven A. Gray, Michigan State University
Fire adapted communities (FAC) are effective when the varied stakeholder groups within them understand the risks of wildfire and take proactive steps to manage these risks. Implementing policies for fire adaptation, however, remains difficult because they are not understood or supported uniformly across diverse stakeholders. To facilitate the creation of FACs, we propose the development of a novel approach, based on Fuzzy Cognitive Maps (FCM), that systematically collects mental model representations from a range of stakeholders to better understand their diverse perceptions of wildfire events, wildfire impacts, and wildfire management and ultimately predicts support for different fire management policies. Further, this information can be used to identify gaps between the risk-related reasoning about wildfire dynamics of fire management experts and the risk-related reasoning about wildfire dynamics of communities exposed to wildfire risks. Applying this method in the context of FAC will serve the needs of decision makers in multiple ways. First, we will be able to predict collaboration and conflict between stakeholder groups by capturing and typifying, (through FCM methodology) stakeholder mental models on wildfire risk exposure and effects. We hypothesize that similarity in mental models between stakeholder groups and between fire management experts is likely to result in collaboration between these groups, while groups with incongruent mental models will have competing policy preferences that could lead to conflict. Information about the degree of homogeneity or heterogeneity of the mental models of stakeholders in a community will support efforts to reach consensus among FACs stakeholders. Second, this research will allow fire management experts to anticipate the future responses to proposed fire management policies. We hypothesize that eliciting the internal mental models of stakeholders and translating these insights into mathematical models of perceptions, based on FCM methodology, will enable policy makers to simulate the outcomes of proposed policies on the stakeholder and community levels. They will thus be able to select policies that are likely to succeed, improve policies that are identified as unlikely to succeed, and prioritize activities based on these policy scenario results. Third we will identify target areas for improving risk communications. Finally, we hypothesize that comparing common stakeholder mental models, represented through FCM, with an expert-generated FCM model, will identify where stakeholder perception about wildfire risks and FAC policies differ from expert opinions. Insights can be used to improve outreach to stakeholders through more tailored risk communication strategies To test these hypotheses we will investigate three research questions: (1) To what degree are stakeholder mental models about wildfire risks homogenous or heterogeneous? (2) Do the differences in stakeholder mental models lead to different predictions about the impacts of wildfire events and different decisions about wildfire management policy support? (3) Does knowledge about differences and similarities in stakeholder mental models improve fire decision-makers' ability to effectively communicate with stakeholders, reach consensus decisions, and implement fire adaptive practices? To answer our research questions we will work closely with a wildfire prone community in the Pacific Northwest in conjunction with wildfire managers at Northwest Fire Science Consortium. The project will result in a general FCM-based social scientific approach that has never before been undertaken in the fire sciences, that is applicable to a range of fire-exposed communities on the urban wildland interface. It will provide one of the first applications of FCM to wildfire management and is the first one to systematically link insights into risk perceptions with concrete policy decision making.
Final report: https://www.frames.gov/catalog/23368