Cooperative game theory and its application to natural, environmental, and water resource issues : 1. basic theory

Game theory provides useful insights into the way parties that share a scarce resource may plan their use of the resource under different situations. This review provides a brief and self-contained introduction to the theory of cooperative games. It can be used to get acquainted with the basics of cooperative games. Its goal is also to provide a basic introduction to this theory, in connection with a couple of surveys that analyze its use in the context of environmental problems and models. The main models (bargaining games, transfer utility, and non-transfer utility games) and issues and solutions are considered: bargaining solutions, single-value solutions like the Shapley value and the nucleolus, and multi-value solutions such as the core. The cooperative game theory (CGT) models that are reviewed in this paper favor solutions that include all possible players and ignore the strategic stages leading to coalition building. They focus on the possible results of the cooperation by answering questions such as: Which coalitions can be formed? And how can the coalitional gains be divided to secure a sustainable agreement? An important aspect associated with the solution concepts of CGT is the equitable and fair sharing of the cooperation gains.Environmental Economics&Policies,Economic Theory&Research,Livestock&Animal Husbandry,Education for the Knowledge Economy,Education for Development (superceded)

Do the Selfish Mimic Cooperators? Experimental Evidence from Finitely-Repeated Labor Markets

Experimental studies have consistently shown that cooperative outcomes can emerge even in finitely repeated games. Such outcomes are justified by existing reputation building models, which suggest that cooperative outcomes can be sustained if some subjects have other-regarding preferences. While the existence of other-regarding preferences is typically used to justify experimental outcomes, we are unaware of empirical studies that explicitly examine the interaction between cooperators (those with other-regarding preferences) and selfish subjects in sustaining cooperation. In this paper, we classify subjects as either selfish or cooperative using simple social preference games and then test for behavioral differences between the two types in a finitely-repeated labor market with unenforceable worker effort. Theory predicts, and our data confirms, that (1) selfish players mimic the actions of cooperators when trading partners can track the individual reputation of past partners and (2) selfish and cooperative types act differently when individual reputations cannot be tracked.contracts, relational contracts, implicit contracts, market interaction, experimental economics, repeated transaction, social preferences, reputation, firm latitude, finitely-repeated games

Reinventing discovery learning: a field-wide research program

© 2017, Springer Science+Business Media B.V., part of Springer Nature. Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective “video games,” and intercorporeal master–student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts

TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al. 2017) on multiple Atari games. Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models