A theoretical foundation for games of complete/incomplete contracts

2021 ◽  
pp. 2150010
Author(s):  
Chenghu Ma ◽  
Wing-Keung Wong
Keyword(s):  
Theoretical Foundation ◽  
Incomplete Contracts ◽  
Final Solution ◽  
Strategic Uncertainty ◽  
Equilibrium Solutions ◽  
Incomplete Contract ◽  
Rational Agents ◽  
Extensive Game ◽  
Agent Interactions ◽  
Multi Agent

This paper provides a theoretical foundation for complete/incomplete contracts to extend game theory for multi-agent interactions. We explain why rational agents may agree to sign incomplete contracts even though signing a complete contract incurs no cost. Some arguments claim that an incomplete contract creates strategic uncertainty. Under common assumptions of rationality, an incomplete contract can be the final solution if the agents’ attitudes toward uncertainty are not neutral. Assuming that agents can form coalitions by participating in the game and they are uncertainty averse, we develop equilibrium solutions for complete/incomplete contracts in an extensive game of multi-agent interactions.

scholarly journals Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning

2020 ◽  
Author(s):  
Ying Wen ◽  
Yaodong Yang ◽  
Jun Wang
Keyword(s):  
Graphical Models ◽  
State Of The Art ◽  
Rational Agents ◽  
Opponent Modeling ◽  
Agent Interactions ◽  
Bayesian Equilibrium ◽  
Thinking Ability ◽  
Beauty Contest ◽  
Multi Agent ◽  
Thinking Process

Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem. In this paper, we introduce generalized recursive reasoning (GR2) as a novel framework to model agents with different \emph{hierarchical} levels of rationality; our framework enables agents to exhibit varying levels of ``thinking'' ability thereby allowing higher-level agents to best respond to various less sophisticated learners. We contribute both theoretically and empirically. On the theory side, we devise the hierarchical framework of GR2 through probabilistic graphical models and prove the existence of a perfect Bayesian equilibrium. Within the GR2, we propose a practical actor-critic solver, and demonstrate its convergent property to a stationary point in two-player games through Lyapunov analysis. On the empirical side, we validate our findings on a variety of MARL benchmarks. Precisely, we first illustrate the hierarchical thinking process on the Keynes Beauty Contest, and then demonstrate significant improvements compared to state-of-the-art opponent modeling baselines on the normal-form games and the cooperative navigation benchmark.

Beyond Citizen Participation in Planning

2010 ◽  
pp. 195-217 ◽  
Author(s):  
Domenico Camarda
Keyword(s):  
Citizen Participation ◽  
Environmental Planning ◽  
Multi Agent Systems ◽  
Formal Representation ◽  
Agent Interactions ◽  
Planning Methods ◽  
Multi Agent ◽  
Planning Processes ◽  
Continuous Feedback ◽  
Problem Setting

The new complexity of planning knowledge implies innovation of planning methods, in both substance and procedure. The development of multi-agent cognitive processes, particularly when the agents are diverse and dynamically associated to their interaction arenas, may have manifold implications. In particular, interesting aspects are scale problems of distributed interaction, continuous feedback on problem setting, language and representation (formal, informal, hybrid, etc.) differences among agents (Bousquet, Le Page, 2004). In this concern, an increasing number of experiences on multi-agent interactions are today located within the processes of spatial and environmental planning. Yet, the upcoming presence of different human agents often acting au paire with artificial agents in a social physical environment (see, e.g., with sensors or data-mining routines) often suggests the use of hybrid MAS-based approaches (Al-Kodmany, 2002; Ron, 2005). In this framework, the chapter will scan experiences on the setting up of cooperative multi-agent systems, in order to investigate the potentials of that approach on the interaction of agents in planning processes, beyond participatory planning as such. This investigation will reflect on agent roles, behaviours, actions in planning processes themselves. Also, an attempt will be carried out to put down formal representation of supporting architectures for interaction and decision making.

FIT BUT: Rational Agents in the Multi-Agent Programming Contest

2021 ◽  
pp. 23-45
Author(s):  
Vaclav Uhlir ◽  
Frantisek Zboril ◽  
Frantisek Vidensky
Keyword(s):  
Rational Agents ◽  
Agent Programming ◽  
Multi Agent

Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics

2009 ◽  
pp. 104-124 ◽  
Author(s):  
Yu Zhang ◽  
Mark Lewis ◽  
Christine Drennon ◽  
Michael Pellon ◽  
Coleman
Keyword(s):  
Decision Making ◽  
Social Structure ◽  
Social Systems ◽  
Three Dimensional ◽  
Decision Making Process ◽  
Social Environments ◽  
Cognitive Agents ◽  
Agent Interactions ◽  
Multi Agent ◽  
Cognitive Decision

Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other diffi- culties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.

Adaptive Collaboration Based on the E-CARGO Model

2012 ◽  
Vol 4 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Haibin Zhu ◽  
Ming Hou ◽  
Mengchu Zhou
Keyword(s):  
Team Performance ◽  
Theoretical Foundation ◽  
Multi Agent Systems ◽  
Role Assignment ◽  
Future Studies ◽  
Agent Systems ◽  
Multi Agent ◽  
Computer Based ◽  
Single Controller ◽  
Role Based

Adaptive Collaboration (AC) is essential for maintaining optimal team performance on collaborative tasks. However, little research has discussed AC in multi-agent systems. This paper introduces AC within the context of solving real-world team performance problems using computer-based algorithms. Based on the authors’ previous work on the Environment-Class, Agent, Role, Group, and Object (E-CARGO) model, a theoretical foundation for AC using a simplified model of role-based collaboration (RBC) is proposed. Several parameters that affect team performance are defined and integrated into a theorem, which showed that dynamic role assignment yields better performance than static role assignment. The benefits of implementing AC are further proven by simulating a “future battlefield” of remotely-controlled robotic vehicles; in this scenario, team performance clearly benefits from shifting vehicles (or roles) using a single controller. Related research is also discussed for future studies.

scholarly journals Bounds and dynamics for empirical game theoretic analysis

2019 ◽  
Vol 34 (1) ◽  
Author(s):  
Karl Tuyls ◽  
Julien Perolat ◽  
Marc Lanctot ◽  
Edward Hughes ◽  
Richard Everett ◽  
...  
Keyword(s):  
Nash Equilibrium ◽  
Evolutionary Dynamics ◽  
Learning Algorithm ◽  
Agent Interactions ◽  
Approximate Nash Equilibrium ◽  
Blotto Game ◽  
Multi Agent ◽  
Payoff Structure ◽  
Game Theoretic ◽  
Colonel Blotto

AbstractThis paper provides several theoretical results for empirical game theory. Specifically, we introduce bounds for empirical game theoretical analysis of complex multi-agent interactions. In doing so we provide insights in the empirical meta game showing that a Nash equilibrium of the estimated meta-game is an approximate Nash equilibrium of the true underlying meta-game. We investigate and show how many data samples are required to obtain a close enough approximation of the underlying game. Additionally, we extend the evolutionary dynamics analysis of meta-games using heuristic payoff tables (HPTs) to asymmetric games. The state-of-the-art has only considered evolutionary dynamics of symmetric HPTs in which agents have access to the same strategy sets and the payoff structure is symmetric, implying that agents are interchangeable. Finally, we carry out an empirical illustration of the generalised method in several domains, illustrating the theory and evolutionary dynamics of several versions of the AlphaGo algorithm (symmetric), the dynamics of the Colonel Blotto game played by human players on Facebook (symmetric), the dynamics of several teams of players in the capture the flag game (symmetric), and an example of a meta-game in Leduc Poker (asymmetric), generated by the policy-space response oracle multi-agent learning algorithm.

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