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Research Spending & Results

Award Detail

Awardee:FLORIDA STATE UNIVERSITY
Doing Business As Name:Florida State University
PD/PI:
  • David J Cooper
  • (850) 644-7097
  • djcooper@fsu.edu
Award Date:07/10/2020
Estimated Total Award Amount: $ 137,981
Funds Obligated to Date: $ 137,981
  • FY 2020=$137,981
Start Date:07/15/2020
End Date:06/30/2023
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: Individual and Team Behavior in Indefinitely Repeated Games
Federal Award ID Number:2018690
DUNS ID:790877419
Parent DUNS ID:159621697
Program:Economics
Program Officer:
  • Nancy Lutz
  • (703) 292-7280
  • nlutz@nsf.gov

Awardee Location

Street:874 Traditions Way, 3rd Floor
City:TALLAHASSEE
State:FL
ZIP:32306-4166
County:Tallahassee
Country:US
Awardee Cong. District:02

Primary Place of Performance

Organization Name:Florida State University
Street:874 Traditions Way, 3rd Floor
City:Tallahassee
State:FL
ZIP:32306-4166
County:Tallahassee
Country:US
Cong. District:02

Abstract at Time of Award

This award funds research using lab experiments to test economic theories about how decision makers interact when the end date of the interactions is uncertain. General Motors competing with Ford and Chrysler is an example of an indefinitely repeated game. “Game” is the technical term in the economics literature for these repeated interactions. This research studies behavior of both individuals and teams of individuals. Most research in this area - theoretical, empirical, and experimental - studies individuals. However, interactions outside the laboratory often involve groups of individuals in a team. Past research shows that economic behavior of individuals can be quite different from the economic behavior of teams. For example, individuals generally follow through on promises to cooperate in short term economic interactions, improving economic welfare. However, teams often act in a self-beneficial manner, necessitating legal penalties to improve welfare. This project will help us understand the differences between teams and individuals in repeated economic interactions. The research method will allow teams to coordinate their actions through written or verbal communication. The research team will analyze these communications as well as actions chose during the experiment. Analyzing communications will provide new insights into what motivates behavior and can identify elements of behavior that had not previously been considered. The results of the project will help managers, policymakers, and citizens understand the effects of working in teams rather than as individuals. There are a number of particular goals for the new research. Game theorists distinguish between perfect and imperfect monitoring. With perfect monitoring, agents can tell what others have done with certainty, but with imperfect monitoring, they are not certain what others have done. The investigators will compare teams with individuals in indefinitely repeated prisoner dilemma (IRPD) games with imperfect monitoring. They are particularly interested in contrasting the effects of team play between perfect and imperfect monitoring, and using the team discussions to help understand how they think about changes in their opponent’s action given that they cannot distinguish between a deliberate choice and random noise, which is inherent in a number of economic interactions. A second project will compare play in IRPD games where continuation probabilities vary across stage games to the standard case with fixed expected end to the interactions. It is clear that discount rates vary over time outside the lab, changing the dynamic incentives agents face. Will subjects account for the change in dynamic incentives and will these changes go beyond what can be explained by the changing dynamic incentives? Previous results suggest that cooperation may emerge for continuation probabilities that normally produce little cooperation. A third project will explore the use of machine learning algorithms to gain greater insights into behavior from the team discussions. There is a growing literature on modern text analysis that may add substantially to what we learn from the team discussions. A fourth project will compare outcomes between individuals and teams in public good games with both monetary punishment and sanctioning of others choices (nonmonetary punishment). The goal here is to determine whether differences between teams and individuals observed in similar settings extend to public good provision for both monetary and non-monetary punishment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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