1. Reward-based decision making under social interaction Damon Tomlin MURI Kick-Off meeting September 13, 2007

2. The decision task A B

3. The underlying structure... 0 0.25 0.5 0.75 1 00.250.50.751 % A Reward Reward A Reward B Average

4. A more interesting case... 0 0.25 0.5 0.75 1 00.250.50.751 % A Reward Reward A Reward B Average

5. Adding social interaction... Feedback –None –Choice history –Individual rewards

6. Adding social interaction Feedback Different games NEO data

7. Conditions in the experiment: “Alone”

8. Conditions in the experiment: “Rewards”

9. Conditions in the experiment: “Choices”

10. Logistics Group size Subject payment Behavioral cohort Imaging cohort

11. Game elements Crossing points Optimal reward Short term vs. long term gains

12. Games within the experiment "Simple" Rising Optimum 0 0.25 0.5 0.75 1 00.250.50.751 % A Reward A Reward B Average Reward How frequently do subjects find the optimum? Once found, do they stay?

13. Games within the experiment "Simple" Rising Optimum 0 0.25 0.5 0.75 1 00.250.50.751 % A Reward A Reward B Average Reward Are subjects naturally biased toward A or B?

14. Games within the experiment “Complex" Rising Optimum 0 0.25 0.5 0.75 1 00.250.50.751 % A Reward A Reward B Average Reward Can subjects find a more subtle strategy? How do social partners affect adherence to it?

15. Individual behavior

17. Games within the experiment Converging Gaussians 0 0.25 0.5 0.75 1 00.250.50.751 % A Reward A Reward B Average Reward How much exploration occurs in a simple task?

18. Individual behavior

19. Games within the experiment Diverging Gaussians 0 0.25 0.5 0.75 1 00.250.50.751 % A Reward A Reward B Average Reward How does social information produce herd behavior?

20. Individual behavior

21. Summary Binary choice decision paradigm Social conditions: –Alone –Reward Information –Choice Information Games examining: –Exploratory behavior –Herd behavior –Strategy maintenance