Co gnitive he uristics for distributed re s ource allocat ion /selection ( COHESION)

1. Cognitive heuristics for distributed resource allocation/selection(COHESION) Ioannis Stavrakakis (University of Athens)

2. Partners • University of Athens (coordinator) • Max Planck Institute for Human Development in Berlin COHESION

3. Max Planck Institute for Human Development • Konstantinos Katsikopoulos • Ph.D in Industrial Engineering and Operations Research, University of Massachusetts, Amherst • Senior research scientist at the Center for Adaptive Behavior and Cognition at the Max Planck Institute • Research Interests: Models of decision-making (prescriptive and descriptive); Applications: economics, management, health and safety COHESION

4. Center for Adaptive Behavior and Cognition • includes psychologists, economists, biologists, mathematicians and computer scientists • work together on the basics of bounded rationality, such as understanding the conditions under which it leads to superior performance, and finding out how often and where such conditions are met. COHESION

5. Key concepts in COHESION Information and communication technologies (ICT) Distributed resource selection settings Autonomous, selfish, bounded-rational decision-makers COHESION

6. ICT-enabled enhanced awareness and decision-making challenges • ICT: Mobile communication devices, sensing platforms, online social applications, etc • Positive effects: • Collection and dissemination of huge amounts of information • This information enriches peoples’ awareness about their environment and its resources (e.g., natural goods, urban space, transportation infrastructure) • Negative effects: • Synchronize the perception of people about the state of resources • Synchronize peoples’ decisions (compete or not compete for the resources)  tragedy-of-commons effects / congestion phenomena COHESION

7. High-level questions • Which is the impact of cognitive mechanisms and heuristics on the efficiency of resource allocation processes (decision-making by autonomous competitors)? • What is the value/impact of the available information on the synchronization / competition phenomena? COHESION

8. Cognitive heuristics • Cognitive heuristics: fast, frugal, adaptive strategies that allow humans to reduce complex decision tasks of predicting, assessing, computing to simpler reasoning processes • Examples of heuristics: recognition, priority, availability, fluency, familiarity, accessibility, representativeness, adjustment-and-anchoring COHESION

9. Recognition heuristic • Recognition heuristic: If there are N alternatives, then rank all n recognized alternatives higher on the criterion under consideration (e.g., which city in a pair has more inhabitants?) than the N-n unrecognized ones • Less-is-more effect: A recognition-dependent agent has a greater probability of choosing the better item than a more knowledgeable agent who recognizes more items *D. G. Goldstein and G. Gigerenzer. Models of ecological rationality: The recognition heuristic. Psychological Review , 109(1):75-90, 2002 COHESION

10. Objectives of the project • To understand the connection between • computational thy of distributed resource allocation systems, • and • cognitive psychological thy of people’s bounded rationality. • For example, explore the effectiveness of sets of individual heuristics that users employ in taking decisions in competitive environments COHESION

11. Methodology • Identify decision heuristics from cognitive psychology literature about human choices among two or more alternatives • Assess how these heuristics affect the efficiency of resource allocation, comparing them with economic norms (e.g., Nash equilibrium) and the optimal central allocation solution • Provide feedback for the way information can be managed to steer human decision-making towards more socially efficient actions COHESION

12. Early results • Study decision making in competitive environments • 2 resource type selection  to compete or not to compete? • e.g., public vs. private parking space -by using classical exp utility theory (game-theoretic tools, Nash equilibrium) to model fully rational agents -by departing from classical exp utility theory to model bounded rational agents: • Considerations (in competing or not competing) • From Behavioral Economics • From Cognitive Psychology • Of cognitive heuristics or biases in user behavior *E. Kokolaki, M. Karaliopoulos, I Stavrakakis, "On the human-driven decision-making process in competitive environments", Internet Science Conference, April 10-11, 2013, Brussels, Best student paper award – Special mention COHESION

13. Considerations of heuristics / biases • Heuristics • satisficing instead of maximization of expected utilities (Simon 1955) • Heuristic strategy for the parking problem • Confidence heuristic: “risk for public parking space according to the probability of winning public parking space” -Satisficing: instead of computing/comparing expected costs, it estimates the probability to hit an empty public spot and plays according to this. COHESION

14. Applying the heuristic model • N agents compete for R=50 (limited) resources that cost 1 unit • Compete (=> go for a limited resource but pay additional cost of 1 units if failed), • Or not compete (=> go for the unlimited resources and pay 3 units) It is shown to yield near-optimal results • It does not take into account the charging costs • It implicitly seeks to avoid tragedy of commons effects COHESION

15. Relevance to EINS JRA activities • This project contributes to the development of a theory of internet science (within JRA 1) • promotes the behavioral/cognitive viewpoint in the activities of task R1.1 “An economics theory for information networks” • As both the available information and the decision making may be shaped by human-driven decision making, this project can also contribute to Task R1.4: Collective Network Intelligence COHESION