We present an approach for implementing a specific form of collaborative industrial practices-called Industrial Symbiotic Networks (ISNs)-as MC-Net cooperative games and address the so called ISN implementation problem. This is, the characteristics of ISNs may lead to inapplicability of fair and stable benefit allocation methods even if the collaboration is a collectively desired one. Inspired by realistic ISN scenarios and the literature on normative multi-agent systems, we consider regulations and normative socioeconomic policies as two elements that in combination with ISN games resolve the situation and result in the concept of coordinated ISNs.
In this paper, we introduce a game-theoretical formulation for a specific form of collaborative industrial relations called Industrial Symbiotic Relation (ISR) games and provide a formal framework to model, verify, and support collaboration decisions in this new class of two-person operational games. ISR games are formalized as cooperative cost-allocation games with the aim to allocate the total ISR-related operational cost to involved industrial firms in a fair and stable manner by taking into account their contribution to the total traditional ISR-related cost. We tailor two types of allocation mechanisms using which firms can implement cost allocations that result in a collaboration that satisfies the fairness and stability properties. Moreover, while industries receive a particular ISR proposal, our introduced methodology is applicable as a managerial decision support to systematically verify the quality of the ISR in question. This is achievable by analyzing if the implemented allocation mechanism is a stable/fair allocation.
This paper discusses the dynamics of Transaction Cost (TC) in Industrial Symbiosis Institutions (ISI) and provides a fair and stable mechanism for TC allocation among the involved firms in a given ISI. In principle, industrial symbiosis, as an implementation of the circular economy paradigm in the context of industrial relation, is a practice aiming at reducing the material/energy footprint of the firm. The well-engineered form of this practice is proved to decrease the transaction costs at a collective level. This can be achieved using information systems for: identifying potential synergies, evaluating mutually beneficial ones, implementing the contracts, and governing the behavior of the established relations. Then the question is how to distribute the costs for maintaining such an information system in a fair and stable manner? We see such a cost as a collective transaction cost and employ an integrated method rooted in cooperative game theory and multiagent systems research to develop a fair and stable allocation mechanism for it. The novelty is twofold: in developing analytical multiagent methods for capturing the dynamics of transaction costs in industrial symbiosis and in presenting a novel game-theoretic mechanism for its allocation in industrial symbiosis institutions. While the former contributes to the theories of industrial symbiosis (methodological contribution), the latter supports decision makers aiming to specify fair and stable industrial symbiosis contracts (practical contribution).
One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward. To satisfy this requirement, existing efforts focus on equilibrium refinement, but the scalability and applicability of existing techniques are limited. In this paper, we propose Temporal-Induced Self-Play (TISP), a novel reinforcement learning-based framework to find strategies with decent performances from any decision point onward. TISP uses belief-space representation, backward induction, policy learning, and non-parametric approximation. Building upon TISP, we design a policy-gradient-based algorithm TISP-PG. We prove that TISP-based algorithms can find approximate Perfect Bayesian Equilibrium in zero-sum one-sided stochastic Bayesian games with finite horizon. We test TISP-based algorithms in various games, including finitely repeated security games and a grid-world game. The results show that TISP-PG is more scalable than existing mathematical programming-based methods and significantly outperforms other learning-based methods.
In this paper we introduce a qualitative decision and game theory based on belief (B) and desire (D) rules. We show that a group of agents acts as if it is maximizing achieved joint goals.
Recent years have witnessed significant advances in technologies and services in modern network applications, including smart grid management, wireless communication, cybersecurity as well as multi-agent autonomous systems. Considering the heterogeneous nature of networked entities, emerging network applications call for game-theoretic models and learning-based approaches in order to create distributed network intelligence that responds to uncertainties and disruptions in a dynamic or an adversarial environment. This paper articulates the confluence of networks, games and learning, which establishes a theoretical underpinning for understanding multi-agent decision-making over networks. We provide an selective overview of game-theoretic learning algorithms within the framework of stochastic approximation theory, and associated applications in some representative contexts of modern network systems, such as the next generation wireless communication networks, the smart grid and distributed machine learning. In addition to existing research works on game-theoretic learning over networks, we highlight several new angles and research endeavors on learning in games that are related to recent developments in artificial intelligence. Some of the new angles extrapolate from our own research interests. The overall objective of the paper is to provide the reader a clear picture of the strengths and challenges of adopting game-theoretic learning methods within the context of network systems, and further to identify fruitful future research directions on both theoretical and applied studies.