Game Theory Relaunched

The game is on. Do you know how to play? Game theory sets out to explore what can be said about making decisions which go beyond accepting the rules of a game. Since 1942, a well elaborated mathematical apparatus has been developed to do so; but there is more. During the last three decades game theoretic reasoning has popped up in many other fields as well - from engineering to biology and psychology. New simulation tools and network analysis have made game theory omnipresent these days. This book collects recent research papers in game theory, which come from diverse scientific communities all across the world; they combine many different fields like economics, politics, history, engineering, mathematics, physics, and psychology. All of them have as a common denominator some method of game theory. Enjoy

Risk communication and participatory research: ‘Fuzzy felt’, visual games and group discussion of complex issues

Published

Application of strategic fuzzy games to wage increase negotiation and decision problems

We propose a flexible decision support scheme which could be used in managing the wage negotiation between employers and employees. This scheme uses fuzzy inference systems and game theory concepts in arriving at decisions on future wage increase which could be more mutually agreeable. For example, rather than specifying 5% yearly increase of wages, we propose that the uncertain factors which are mostly difficult to predict and that could affect wage decisions need to be taken into consideration by the wage formula. These include business revenues or (profit), inflation rate, number of competitors, cost of production, and other uncertain factors that may affect business operations. The accuracy of the fuzzy rule base and the game strategies will help to mitigate the adverse effects that a business may suffer from these uncertain factors. Based on our scheme, we propose that employers and employees should calculate their future wage by using a fuzzy rule base and strategies that take into consideration these uncertain variables. The proposed approach is illustrated with a case study and the procedure and methodology may be easily implemented by business organizations in their wage bargaining and decision processes. © 2012 Elsevier Ltd. All rights reserved

Abstracts of Invited and Selected Papers and Organized Symposia, WAEA Annual Meetings, San Francisco, California, July 6-8, 2005

Teaching/Communication/Extension/Profession,

Network design and technology management for waste to energy production:An integrated optimization framework under the principles of circular economy

The design of waste to bioenergy supply chains (W-BESC) is critically important for meeting the circular economy (CE) goals, whilst also ensuring environmental sustainability in the planning and operation of energy systems. This study develops a novel optimization methodology to aid sustainable design and planning of W-BESC that comprise multiple technologies as well as multiple product and feedstock types. The methodology identifies the optimum supply chain configuration and plans the logistics operations in a given region to meet the energy demand of specified nodes. A scenario based fuzzy multi objective modelling approach is proposed and utilized to capture the economic and environmental sustainability aspects in the same framework. We test the proposed model using the entire West Midlands (WM) region from the United Kingdom (UK) as a case study. In this scope, a comprehensive regional supply chain is designed to meet the energy and biofertilizer demand of specific nodes considering available waste and crop type biomass in the region. Further analysis is conducted to reveal the impacts of main economic and technological parameters on the supply chain performance indicators

Big Data Analysis-based Security Situational Awareness for Smart Grid

Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness

Multi-item inventory policy with time-dependent pricing and rework cost

The price of broiler chickens at the consumer level varies daily. The price can be very low or otherwise. The price has resulted from the imbalance between the availability of chicken from suppliers and the market demand. As a result, demand will also fluctuate because it is influenced by consumer purchasing power. When the price of live chickens is low, the carcass company will usually buy in large quantities and expect to sell them at a higher price. The problem arises when the chicken overstock company will risk product damage due to product buildup in the refrigerated warehouse, so rework is necessary. In this paper, we will be developed a multi-item inventory model that considers material prices that vary to time, probabilistic demand, and rework costs. The aim is to determine the right policy for controlling frozen chicken products' inventory to minimize losses and total inventory costs.  This model can evaluate the best time to order broiler chickens, how much to order, how long the interval between orders, and the optimal number of orders, resulting in minimum total inventory cost per period.  The model solution is carried out with an optimization approach based on the parameters that affect the model. A numerical example is given at the end of this paper for model validation and illustrates the model solving algorithm

A Temporal Framework for Hypergame Analysis of Cyber Physical Systems in Contested Environments

Game theory is used to model conflicts between one or more players over resources. It offers players a way to reason, allowing rationale for selecting strategies that avoid the worst outcome. Game theory lacks the ability to incorporate advantages one player may have over another player. A meta-game, known as a hypergame, occurs when one player does not know or fully understand all the strategies of a game. Hypergame theory builds upon the utility of game theory by allowing a player to outmaneuver an opponent, thus obtaining a more preferred outcome with higher utility. Recent work in hypergame theory has focused on normal form static games that lack the ability to encode several realistic strategies. One example of this is when a player’s available actions in the future is dependent on his selection in the past. This work presents a temporal framework for hypergame models. This framework is the first application of temporal logic to hypergames and provides a more flexible modeling for domain experts. With this new framework for hypergames, the concepts of trust, distrust, mistrust, and deception are formalized. While past literature references deception in hypergame research, this work is the first to formalize the definition for hypergames. As a demonstration of the new temporal framework for hypergames, it is applied to classical game theoretical examples, as well as a complex supervisory control and data acquisition (SCADA) network temporal hypergame. The SCADA network is an example includes actions that have a temporal dependency, where a choice in the first round affects what decisions can be made in the later round of the game. The demonstration results show that the framework is a realistic and flexible modeling method for a variety of applications