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Embedded Files
Atkinson Behavioral Research Lab at UCSD Rady School of Management to conduct research in request of professors.
Conducted behavioral research via proctoring digital surveys, leading focus group studies, and operating interviews.
Operated SONA Research Management System to record research participants attendance and their research results.
Studied the requested research topics and procedures of the professors and communicated about expected behaviors.
Communicated with Lab PI Dr. Karmarkar about Neuroeconomics Research to drive accurate consumer knowledge.
Current Position: Behavioral Research Assistant - [June 2024 - May 2025]
⟐ HOLISTIC FRAMEWORK: BEHAVIORAL RESEARCH ⟐
➣ OBJECTIVE #1:
SETTING AREA OF EXPLORATION FOR BUSINESS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
➣ OBJECTIVE #2:
HYPOTHESIS TEST AND EXPERIMENTAL DESIGNS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
➣ OBJECTIVE #3:
EXPERIMENTAL DATA ANALYSIS AND STATISTICS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
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MAIN PAGE #1: BEHAVIORAL RESEARCH + MARKET RESEARCH METHODS
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⟐ RESEARCH METHODS: EXPERIMENT OPERATION TECHNIQUES ⟐
➣ OBJECTIVE #1:
SETTING AREA OF EXPLORATION FOR BUSINESS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
➣ OBJECTIVE #2:
HYPOTHESIS TEST AND EXPERIMENTAL DESIGNS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
➣ OBJECTIVE #3:
EXPERIMENTAL DATA ANALYSIS AND STATISTICS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
CONDUCTING DIGITAL SURVEY METHODOLOGIES: __________________________________________________________________________
TUTORIAL FOR MANAGING DIGITAL SURVEY PER SESSION & SURVEY EXPERIMENTS OPERATED
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QUALTRICS SURVEY EXPERIMENTAL DESIGN
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CONDUCTED SURVEYS AND CORRESPONDING RESEARCH TOPIC
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QUALTRICS SURVEY INFORMATION SESSION
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CONTENT #1
Designing Qualtrics
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Research Ethics Checklist
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Survey Accuracy #1: Attention Check
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Survey Accuracy #2: Double Blind Experiments
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Survey Accuracy #3: Preregistration to Avoid Subconscious Bias
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Survey Accuracy #4: Understanding Bias and Variance
⇒ Bias: High bias means that the responses is not correct on average, whether it is reported as higher number or lower number than the truth. ⇐
⇒ Variance: The spread or variability in responses where high variance means responses are widely dispersed but the average value is correct. ⇐
⇒ Best to Worst Responses: [1] Low Bias & Low Variance [2] Low Bias & High Variance [3] High Bias & Low Variance [4] Low Bias & Low Variance ⇐
Survey Accuracy #5: Neutrality, Understandability, and Ambiguity
⇒ Neutrality: The question should be balanced and not lead respondents towards a particular answer with implied bias, e.g., "how amazing was..." leads into bias. ⇐
⇒ Understandability: The question should be clear and easy for respondents to comprehend to avoid confusion, e.g., long paragraphs is often not understandable. ⇐
⇒ Ambiguity: The response options should not be vague, overlapping, or open to multiple interpretations, e.g., asking food, service, and ambience together is unclear. ⇐
AB Testing and Randomized Controlled Trial [RCT]: Control Condition & Treatment Condition
⇒ AB Test: An experiment wher two or more variants (A and B) are compared to determine which one performs better. The audience is randomly split into groups, with one group experiencing Version A (Control Group) and another group experiencing Version B (Treatment Group). The results are analyzed based on predefined DV, i.e., KPI. ⇐
⇒ Randomized Controlled Trial (RCT): An experiment to evaluate an effectiveness of an intervention. The participants are randomly assigned to either the control group--receiving a standard treatment--or the treatment group--receiving the intervention. The experiment checks whether the differences in DV is due to intervention itself. ⇐
⇒ Control Group: A group which does not receive the experimental treatment or intervention, serving as a baseline for comparison against the effect of an intervention. ⇐
⇒ Treatment Group: A group which receives the treatment or intervention which is being tested, serving with the test outcomes to assess the impact of the intervention. ⇐
⇒ Random Assignment: Ensures that the groups on average are similar in all ways (gender breakdown, health, wealth, etc) except for the independent variable. ⇐
[Ex. 1] Dr. Karmarkar Survey Research on "The Unlikelihood Effect: When Knowing More Creates the Perception of Less"
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[Ex. 2] Dr. Karmarkar Survey Research on "Add to a Base Model vs. Subtract from a Full Model"
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Sample Survey: Making Survey on Qualtrics
⇒ Adding Requirements | Adding Validation [make sure the answers meet the condition ex: passcode or number of characters] ⇐
⇒ Get authorization from IRB [International Research Board] | Consent Form ⇐
⇒ Question Type: Timing until going to next page | ⇐
⇒ ATTENTION CHECK: People who didn't pay attention will not be considered in the data set ⇐
⇒ Randomizer Functionality for Showing Different Conditions ⇐
⇒ Then Branch If: [1] if a person did not focus and got caught [2] Same Sona ID in a week for same survey ⇐
⇒ DOUBLE BLIND EXPERIMENTS: PARTICIPANTS AND RESEARCHERS DO NOT KNOW THE RESEARCH DETAILS ⇐
RCT
⇒ Control vs Treatment Condition | Condition 1 vs. Condition 2 ⇐
Avoiding Subconscious Bias: Preregistration Process
⇒ Deleting Data --> They set standard of how they are going to delete data that don't pass attention check. ⇐
⇒ ATTENTION CHECK: Reverse Coding | Not passing the attnetion test data gets deleted. ⇐
Ex 1: Dr. Karmarkar Research about THE UNLIKELIHOOD EFFECT
⇒ Risk of Survey: Developing a bacterial infection from fleas. ⇐
⇒ 3 Conditions [0] only about 1 flea with percentage (58%) [1] about all types of fleas with percentage (58%) (breaking down 58% into different types of fleas) [2] all fleas without percentage (breaking down types of fleas without percentages) ⇐
⇒ Prediction: Condition 0 is the highest likelihood of getting infected | 1 is second less likely | 2 is higher likelihood ⇐
⇒ Hypothesis: When you tell people lower probability for each of them, people will think likelihood of getting risk is less ⇐
⇒ Previous Research: More reasons lead into increased likelihood of risk | Karmarkar Research: More reasons with lower probability decrease likelihood of risk. ⇐
Ex 2: Dr. Karmarkar: $5 Accept or Reject Conditions
⇒ Reject Condition: 6 items are included in the basket and participants decide which items to take out of the basket. [Keep] ⇐
⇒ Accept Condition: $5 dollars are spent to add items to the basket from 6 items [Add] ⇐
⇒ "Add to a base model" vs. "Subtract from a full model" ⇐
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CONDUCTING FOCUS GROUP METHODOLOGIES: ___________________________________________________________________________
FOCUS GROUP
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CONDUCTING IN-DEPTH INTERVIEW METHODOLOGIES: _____________________________________________________________________
IN-DEPTH INTERVIEW
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CONDUCTING BEHAVIORAL TESTING METHODOLOGIES: _____________________________________________________________________
IN-DEPTH INTERVIEW
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RESEARCH ASSISTANT TRAINING MANUAL: ________________________________________________________________________________
RA TRAINING MANUAL
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SURVEY RESEARCH EXAMPLE: [i] __________________________________________ [ii] ___________________________________________
[DIGITAL VS BRAND] [COMMERCIAL] [TERRITORY] AND ROLES
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FOCUS GROUP RESEARCH EXAMPLE: [i] __________________________________________ [ii] _____________________________________
[DIGITAL VS BRAND] [COMMERCIAL] [TERRITORY] AND ROLES
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⟐ RESEARCH STUDY DESIGNS: RESEARCH DESIGN TYPES REQUESTED ⟐
➣ OBJECTIVE #1:
SETTING AREA OF EXPLORATION FOR BUSINESS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
➣ OBJECTIVE #2:
HYPOTHESIS TEST AND EXPERIMENTAL DESIGNS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
➣ OBJECTIVE #3:
EXPERIMENTAL DATA ANALYSIS AND STATISTICS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
RANDOMIZED CONTROLLED TRIAL: ______________________________________________________________________________________
TUTORIAL FOR MANAGING DIGITAL SURVEY PER SESSION & SURVEY EXPERIMENTS OPERATED
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QUALTRICS SURVEY EXPERIMENTAL DESIGN
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CONDUCTED SURVEYS AND CORRESPONDING RESEARCH TOPIC
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QUALTRICS SURVEY INFORMATION SESSION
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Designing Qualtrics
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Research Ethics Checklist
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Survey Accuracy #1: Attention Check
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Survey Accuracy #2: Double Blind Experiments
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Survey Accuracy #3: Preregistration to Avoid Subconscious Bias
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Survey Accuracy #4: Understanding Bias and Variance
⇒ Bias: High bias means that the responses is not correct on average, whether it is reported as higher number or lower number than the truth. ⇐
⇒ Variance: The spread or variability in responses where high variance means responses are widely dispersed but the average value is correct. ⇐
⇒ Best to Worst Responses: [1] Low Bias & Low Variance [2] Low Bias & High Variance [3] High Bias & Low Variance [4] Low Bias & Low Variance ⇐
Survey Accuracy #5: Neutrality, Understandability, and Ambiguity
⇒ Neutrality: The question should be balanced and not lead respondents towards a particular answer with implied bias, e.g., "how amazing was..." leads into bias. ⇐
⇒ Understandability: The question should be clear and easy for respondents to comprehend to avoid confusion, e.g., long paragraphs is often not understandable. ⇐
⇒ Ambiguity: The response options should not be vague, overlapping, or open to multiple interpretations, e.g., asking food, service, and ambience together is unclear. ⇐
AB Testing and Randomized Controlled Trial [RCT]: Control Condition & Treatment Condition
⇒ AB Test: An experiment wher two or more variants (A and B) are compared to determine which one performs better. The audience is randomly split into groups, with one group experiencing Version A (Control Group) and another group experiencing Version B (Treatment Group). The results are analyzed based on predefined DV, i.e., KPI. ⇐
⇒ Randomized Controlled Trial (RCT): An experiment to evaluate an effectiveness of an intervention. The participants are randomly assigned to either the control group--receiving a standard treatment--or the treatment group--receiving the intervention. The experiment checks whether the differences in DV is due to intervention itself. ⇐
⇒ Control Group: A group which does not receive the experimental treatment or intervention, serving as a baseline for comparison against the effect of an intervention. ⇐
⇒ Treatment Group: A group which receives the treatment or intervention which is being tested, serving with the test outcomes to assess the impact of the intervention. ⇐
⇒ Random Assignment: Ensures that the groups on average are similar in all ways (gender breakdown, health, wealth, etc) except for the independent variable. ⇐
[Ex. 1] Dr. Karmarkar Survey Research on "The Unlikelihood Effect: When Knowing More Creates the Perception of Less"
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[Ex. 2] Dr. Karmarkar Survey Research on "Add to a Base Model vs. Subtract from a Full Model"
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CONTENT #2
Sample Survey: Making Survey on Qualtrics
⇒ Adding Requirements | Adding Validation [make sure the answers meet the condition ex: passcode or number of characters] ⇐
⇒ Get authorization from IRB [International Research Board] | Consent Form ⇐
⇒ Question Type: Timing until going to next page | ⇐
⇒ ATTENTION CHECK: People who didn't pay attention will not be considered in the data set ⇐
⇒ Randomizer Functionality for Showing Different Conditions ⇐
⇒ Then Branch If: [1] if a person did not focus and got caught [2] Same Sona ID in a week for same survey ⇐
⇒ DOUBLE BLIND EXPERIMENTS: PARTICIPANTS AND RESEARCHERS DO NOT KNOW THE RESEARCH DETAILS ⇐
RCT
⇒ Control vs Treatment Condition | Condition 1 vs. Condition 2 ⇐
Avoiding Subconscious Bias: Preregistration Process
⇒ Deleting Data --> They set standard of how they are going to delete data that don't pass attention check. ⇐
⇒ ATTENTION CHECK: Reverse Coding | Not passing the attnetion test data gets deleted. ⇐
Ex 1: Dr. Karmarkar Research about THE UNLIKELIHOOD EFFECT
⇒ Risk of Survey: Developing a bacterial infection from fleas. ⇐
⇒ 3 Conditions [0] only about 1 flea with percentage (58%) [1] about all types of fleas with percentage (58%) (breaking down 58% into different types of fleas) [2] all fleas without percentage (breaking down types of fleas without percentages) ⇐
⇒ Prediction: Condition 0 is the highest likelihood of getting infected | 1 is second less likely | 2 is higher likelihood ⇐
⇒ Hypothesis: When you tell people lower probability for each of them, people will think likelihood of getting risk is less ⇐
⇒ Previous Research: More reasons lead into increased likelihood of risk | Karmarkar Research: More reasons with lower probability decrease likelihood of risk. ⇐
Ex 2: Dr. Karmarkar: $5 Accept or Reject Conditions
⇒ Reject Condition: 6 items are included in the basket and participants decide which items to take out of the basket. [Keep] ⇐
⇒ Accept Condition: $5 dollars are spent to add items to the basket from 6 items [Add] ⇐
⇒ "Add to a base model" vs. "Subtract from a full model" ⇐
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QUASI-EXPERIMENTAL DESIGN: _________________________________________________________________________________________
FOCUS GROUP
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OBSERVATIONAL STUDIES: _____________________________________________________________________________________________
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A
⟐ RESEARCH SEMINARS: ATTENDING PRESENTATIONS OF BUSINESS RESEARCH ⟐
➣ OBJECTIVE #1:
SETTING AREA OF EXPLORATION FOR BUSINESS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
➣ OBJECTIVE #2:
HYPOTHESIS TEST AND EXPERIMENTAL DESIGNS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
➣ OBJECTIVE #3:
EXPERIMENTAL DATA ANALYSIS AND STATISTICS
전체적으로 참여를 하는 사람들에게 너무 일이 집중되고 거의 아무것도 안하는 사람들의 비율이 좋지 않았다. 나는 그것의 문제를 멤버들이 본인이 당장 팀에 어차피 그게 기여를 안하고 있다고 생각해서라고 판단함.
RESEARCH SEMINAR - MARKETING: ASSORTMENT CURATION IN ONLINE MARKETPLACES - ANTONIO MORENO AT HARVARD BUSINESS SCHOOL
⟐ RESEARCH SEMINAR: MARKETING ⟐
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⇒ Assortment Curation in Online Marketplaces - Professor Antonio Moreno at Harvard Business School [온라인 시장에서의 제품 구성 선별 전략] ⇐
⇒ Digital Media Mergers: Theory and Application to Facebook-Instagram - ______________ [_______] ⇐
⇒ Privacy-Enhanced versus Traditional Retargeting: Ad Effectiveness in an Industry-Wide Field Experiment [_______] ⇐
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ASSORTMENT CURATION IN ONLINE MARKETPLACES - PROFESSOR ANTONIO MORENO AT HARVARD BUSINESS SCHOOL - APRIL 11, 2025
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DIGITAL MEDIA MERGERS: THEORY AND APPLICATION TO FACEBOOK-INSTAGRAM
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Motivation
⇒ concerns about digital platforms' market power and appropriate competition policy. ⇐
⇒ Key Example: Facebook-Instagram [i] FTC sued Meta for illegally monopolizing "personal social networking" ⇐
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R.Q.
⇒ Theory: How do digital media mergers (or separations) affect ad markets and distribution of surplus? ⇐
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PRIVACY-ENHANCED VERSUS TRADITIONAL RETARGETING: AD EFFECTIVENESS IN AN INDUSTRY-WIDE FIELD EXPERIMENT
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RESEARCH SEMINAR - MANAGEMENT: COALITION-BUILDING IN A DIVERSIFYING NATION - PROFESSOR MAUREEN CRAIG AT DUKE UNIVERSITY__
⟐ RESEARCH SEMINAR: MANAGEMENT ⟐
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⇒ Coalition-Building in a Diversifying Nation - Professor Maureen Craig at Duke University [다문화 국가에서의 연대 형성] ⇐
⇒ How Network-Central People Shape Group Outcomes: Professor Adam Kleinbaum at Dartmouth College [네트워크 중심 인물이 집단의 결과에 미치는 영향] ⇐
⇒ ____________ ⇐
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COALITION-BUILDING IN A DIVERSIFYING NATION - PROFESSOR MAUREEN CRAIG AT DUKE UNIVERSITY - APRIL 21, 2025
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How Policy Preferences Are Typucally Measured:
⇒ ____________ ⇐
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HOW NETWORK-CENTRAL PEOPLE SHAPE GROUP OUTCOMES: PROFESSOR ADAM KLEINBAUM AT DARTMOUTH COLLEGE - JANUARY 27, 2025
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RESEARCH SEMINAR - ECONOMIC STRATEGIES: ____________________________________________________________________________
⟐ RESEARCH SEMINAR: ECONOMIC STRATEGY ⟐
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⇒ Behavioral Incentive Compatibility: Testing Incentives - Professor Lise Vesterlund at University of Pittsburgh [행동 기반 인센티브 정합성: 인센티브 메커니즘 실험] ⇐
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BEHAVIORAL INCENTIVE COMPATIBILITY: TESTING INCENTIVES - PROFESSOR LISE VESTERLUND AT UNIVERSITY OF PITTSBURGH - OCTOBER 22, 2024
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Research Summary
⇒ The idea behind Behavioral Incentive Compatibility in Vesterlund Research is to create settings where people are naturally incentivized to act in a way that reveals their true behavior, providing more reliable insights into how incentives drive human actions. ⇐
Reverse Game Theory
⇒ How to identify desirable outcome and work backwards to select institutions that implement outcome [e.g. auction bidding rules, a matching algorithms] ⇐
⇒ Behavioral Incentive Compatibility (BIC) refers to the design of mechanisms or experiments in a way that aligns participants' incentives with the desired outcomes, encouraging them to act truthfully or in a way that reveals their true preferences. When testing incentives, the goal is to ensure that individuals' behavior reflects their genuine attitudes, preferences, or motivations rather than strategic manipulation. ⇐
⇒ Examines effectiveness of incentive structures in various economic settings, including public goods, gender differences in competition, and charitable giving. ⇐
Principles When Testing Incentives Under BIC
⇒ Aligning Interests: Ensure that participants have no incentive to misrepresent their preferences or behavior. For example, if the goal is to measure genuine willingness to pay for a product, the incentive mechanism should be designed so that the best strategy for participants is to state their true valuation. ⇐
⇒ Avoiding Strategic Behavior: Participants should not benefit from manipulating their actions. A well-designed BIC experiment ensures that behaving honestly or revealing true preferences yields the best outcome for participants. ⇐
⇒ Real-world Applications: Vesterlund's research often extends to real-world scenarios, such as understanding how people behave in auctions, negotiations, or fundraising settings. For example, if designing an auction, making the mechanism incentive-compatible would mean ensuring bidders bid their true value rather than bluffing or underbidding. ⇐
⇒ Testing Different Incentive Structures: Experiments can be set up to see how changes in incentives (like rewards, penalties, or information availability) alter behavior. This helps identify which structures lead to desired outcomes, like increased contributions to public goods or fairer competition. ⇐
Design Mechanism: Binary Scoring Rule
⇒ BSR stands for Binary Scoring Rule, The Binarized Scoring Rule is a method used in experimental economics to elicit truthful reporting of beliefs or preferences. It transforms a participant’s continuous or probabilistic assessment into a binary (yes/no) outcome. Essentially, it uses a scoring rule to gauge how accurate or honest a participant's response is and then "binarizes" it to create a clear incentive for truthfulness. ⇐
⇒ Scoring Mechanism: Participants provide a probabilistic assessment or valuation, such as their belief about the likelihood of an event or their willingness to pay for a product. The scoring rule is designed to reward accurate or truthful reporting. ⇐
⇒ Binarization: Instead of directly using the continuous score, the mechanism converts the scoring into a binary outcome, like winning or losing a reward, based on the participant’s input. This incentivizes participants to provide responses that reflect their true beliefs or values because a payoff depends on the accuracy of their response. ⇐
⇒ Rate of Mistakes Compared to QSR [Quadratic Scoring Rule] is much lower in BSR. ⇐
Experimental Method: Pure Incentives Testing
⇒ A pure incentives test is an experimental method used to determine whether the incentives provided in a mechanism or system are sufficient to drive the desired behavior without any external influences or confounding factors. The main goal of a pure incentives test is to isolate the effect of the incentives themselves, ensuring that the behavior observed in the experiment is a direct response to the incentives rather than other variables. ⇐
⇒ Focus on Incentive Structures: The test aims to see if the incentives provided (like rewards, payments, or penalties) are aligned with the outcomes the experimenter wants to achieve. For instance, if the goal is to encourage participants to reveal their true preferences, the incentives should be structured so that truth-telling is the most beneficial strategy for them. ⇐
⇒ Elimination of Confounding Factors: The environment is controlled to eliminate other factors that might influence behavior, such as social pressures, information asymmetry, or strategic manipulation. This ensures that any behavior change can be attributed to the incentive itself. ⇐
⇒ Behavioral Consistency: By focusing purely on the incentives, researchers can assess whether participants are behaving in a way that is consistent with what would be expected from a rational, utility-maximizing individual. If the incentives are correctly designed, participants should naturally align their actions to maximize their rewards or minimize costs. ⇐
⇒ Real-world Applicability: The insights from pure incentives tests help in designing real-world mechanisms where incentives need to drive specific actions. Examples include pricing strategies, auctions, contract designs, and public policy implementations. ⇐
Summary
⇒ Behaviorally inventive compatibility critical for implementation. ⇐
⇒ Propose Pure Incentives Tests: Direct test of the IC constraint specified under the mechanism. ⇐
⇒ 1) assess whether 'in your best interest' statement justified 2) assess bias under mechanism 3) point to cause of deviation 4) a necessary condition for BIC [alternate presentation of incentives?] ⇐
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