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1. Using Team Discussions to Understand Behavior in Indefinitely Repeated Prisoner’s Dilemma Games

   David J. Cooper; John H. Kagel (November 2023, American economic journal Microeconomics)

   We compare behavior of two person teams with individuals in indefinitely repeated prisoner dilemma games with perfect monitoring. Team discussions are used to understand the rationale underlying these choices, and how these choices come about. There are three main findings: (1) Teams learned to cooperate faster than individuals, and cooperation was more stable for teams. (2) Strategies identified from team dialogues differ from those identified by the Strategy Frequency Estimation Method. This reflects the improvisational nature of teams’ decision making. (3) Increasing cooperation was primarily driven by teams unilaterally cooperating in the hope of inducing their opponent to cooperate. 

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2. Using team discussions to understand behavior in indefinitely repeated prisoner dilemma games

   John H. Kagel; David J Cooper (August 2024, American economic journal Microeconomics)

   We compare behavior of two person teams with individuals in indefinitely repeated prisoner dilemma games with perfect monitoring. Team discussions are used to understand the rationale underlying these choices, and how these choices come about. There are three main findings: (1) Teams learned to cooperate faster than individuals, and cooperation was more stable for teams. (2) Strategies identified from team dialogues differ from those identified by the Strategy Frequency Estimation Method. This reflects the improvisational nature of teams’ decision making. (3) Increasing cooperation was primarily driven by teams unilaterally cooperating in the hope of inducing their opponent to cooperate. 

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3. Using Team Discussions to Understand Behavior in Indefinitely Repeated Prisoner’s Dilemma Games

   Cooper, David J. (December 2023, American economic journal Microeconomics)

   See journal issue (Ed.)

   We compare behavior of two person teams with individuals in indefinitely repeated prisoner dilemma games with perfect monitoring. Team discussions are used to understand the rationale underlying these choices, and how these choices come about. There are three main findings: (1) Teams learned to cooperate faster than individuals, and cooperation was more stable for teams. (2) Strategies identified from team dialogues differ from those identified by the Strategy Frequency Estimation Method. This reflects the improvisational nature of teams’ decision making. (3) Increasing cooperation was primarily driven by teams unilaterally cooperating in the hope of inducing their opponent to cooperate. 

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4. Using Team Discussions to Understand Behavior in Indefinitely Repeated Prisoner’s Dilemma Games

   https://doi.org/10.1257/mic.20210012  

   Cooper, David J; Kagel, John H (November 2023, American Economic Journal: Microeconomics)

   We compare behavior of two person teams with individuals in indefinitely repeated prisoner dilemma games with perfect monitoring. Team discussions are used to understand the rationale underlying these choices and how these choices come about. There are three main findings: (i) Teams learned to cooperate faster than individuals, and cooperation was more stable for teams. (ii) Strategies identified from team dialogues differ from those identified by the Strategy Frequency Estimation Method. This reflects the improvisational nature of teams’ decision making. (iii) Increasing cooperation was primarily driven by teams unilaterally cooperating in the hope of inducing their opponent to cooperate. (JEL C72, C73, C92) 

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5. Ad Hoc Teaming Through Evolution

   Cook, Joshua; Tumer, Kagan (July 2021, Genetic and evolutionary computation)

   null (Ed.)

   Cooperative Co-evolutionary Algorithms effectively train policies in multiagent systems with a single, statically defined team. However, many real-world problems, such as search and rescue, require agents to operate in multiple teams. When the structure of the team changes, these policies show reduced performance as they were trained to cooperate with only one team. In this work, we solve the cooperation problem by training agents to fill the needs of an arbitrary team, thereby gaining the ability to support a large variety of teams. We introduce Ad hoc Teaming Through Evolution (ATTE) which evolves a limited number of policy types using fitness aggregation across multiple teams. ATTE leverages agent types to reduce the dimensionality of the interaction search space, while fitness aggregation across teams selects for more adaptive policies. In a simulated multi-robot exploration task, ATTE is able to learn policies that are effective in a variety of teaming schemes, improving the performance of CCEA by a factor of up to five times. 

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