On the Size and Structure of Group Cooperation

This paper examines characteristics of cooperative behavior in a repeated, n-person, continuous action generalization of a Prisoner’s Dilemma game. When time preferences are heterogeneous and bounded away from one, how “much” cooperation can be achieved by an ongoing group? How does group cooperation vary with the group’s size and structure? For an arbitrary distribution of discount factors, we characterize the maximal average co-operation (MAC) likelihood of this game. The MAC likelihood is the highest average level of cooperation, over all stationary subgame perfect equilibrium paths, that the group can achieve. The MAC likelihood is shown to be increasing in monotone shifts, and decreasing in mean preserving spreads, of the distribution of discount factors. The latter suggests that more heterogeneous groups are less cooperative on average. Finally, we establish weak conditions under which the MAC likelihood exhibits increasing returns to scale when discounting is heterogeneous. That is, larger groups are more cooperative, on average, than smaller ones. By contrast, when the group has a common discount factor, the MAC likelihood is invariant to group size.Repeated games, Maximal average Cooperation likelihood, Heterogeneous discount factors, Returns to scale

Social status in economic theory: a review.

Social distinction or status is an important motivation of human behaviour. This paper provides a selective survey of recent advances in the economic analysis of the origins and consequences of social status. First, a selection of empirical research from a variety of scientific disciplines is discussed to underpin the further theoretical analysis. I then consider the origins and determinants of tastes for status, discuss the endogenous derivation of such a preferences for relative standing and assess the different formalisations these preferences. Subsequently, the consequences of preferences for status are studied for a variety of problems and settings. The last section discusses a number of implications of status concerns for normative economics and public policy.

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Learning Curricula in Open-Ended Worlds

Deep reinforcement learning (RL) provides powerful methods for training optimal sequential decision-making agents. As collecting real-world interactions can entail additional costs and safety risks, the common paradigm of sim2real conducts training in a simulator, followed by real-world deployment. Unfortunately, RL agents easily overfit to the choice of simulated training environments, and worse still, learning ends when the agent masters the specific set of simulated environments. In contrast, the real-world is highly open-ended—featuring endlessly evolving environments and challenges, making such RL approaches unsuitable. Simply randomizing across a large space of simulated environments is insufficient, as it requires making arbitrary distributional assumptions, and as the design space grows, it can become combinatorially less likely to sample specific environment instances that are useful for learning. An ideal learning process should automatically adapt the training environment to maximize the learning potential of the agent over an open-ended task space that matches or surpasses the complexity of the real world. This thesis develops a class of methods called Unsupervised Environment Design (UED), which seeks to enable such an open-ended process via a principled approach for gradually improving the robustness and generality of the learning agent. Given a potentially open-ended environment design space, UED automatically generates an infinite sequence or curriculum of training environments at the frontier of the learning agent’s capabilities. Through both extensive empirical studies and theoretical arguments founded on minimax-regret decision theory and game theory, the findings in this thesis show that UED autocurricula can produce RL agents exhibiting significantly improved robustness and generalization to previously unseen environment instances. Such autocurricula are promising paths toward open-ended learning systems that approach general intelligence—a long sought-after ambition of artificial intelligence research—by continually generating and mastering additional challenges of their own design