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Reward-Based Decision Making Under Social Interaction: Insights from MURI Kick-Off Meeting

The MURI Kick-Off Meeting held on September 13, 2007, focused on the dynamics of reward-based decision making in contexts of social interaction. The discussion revolved around how various conditions, such as feedback and individual rewards, influence decision-making processes in games designed to examine exploratory and herd behavior. The experiment evaluated subjects' tendencies to pursue optimal rewards, their choice history, and the impact of social partners on decision adherence. Key elements included individual behaviors in simple and complex tasks, emphasizing the role of social context in decision making.

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Reward-Based Decision Making Under Social Interaction: Insights from MURI Kick-Off Meeting

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

  2. A B The decision task

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

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

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

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

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

  15. Individual behavior

  16. Individual behavior

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

  18. Individual behavior

  19. Reward A Reward B Average Games within the experiment Diverging Gaussians 1 0.75 Reward 0.5 0.25 0 0 0.25 0.5 0.75 1 % A 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

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