Assessment of Adaptability of a Supply Chain Trading Agent’s Strategy: Evolutionary Game Theory Approach

With the increase in the complexity of supply chain management, the use of intelligent agents for automated trading has gained popularity (Collins, Arunachalam, B, et al. 2006). The performance of supply-chain agents depends on not just the market environment (supply and demand patterns) but also on what types of other agents they are competing with. For designers of such agents it is important to ascertain that their agents are robust and can adapt to changing market and competitive environments. However, to date there has not been any work done that assesses the adaptability of a trading agent’s strategy in the presence of various demand and supply distributions when competing with a changing composition of agents using different strategies. In this paper we use the concept of replicator dynamics to study the evolution of a population of strategies used by supply chain agents when the different agents are competing against each other. We also study the evolution of the population of agents’ strategies in the presence of six types of adverse market conditions. In particular we test three strategies that have been presented in the literature and our results indicate that over time supply chain agents gravitate towards using the SCMaster strategy in most scenarios.

[1]  Norman Sadeh,et al.  The Supply Chain Management Game for the Trading Agent Competition 2004 , 2004 .

[2]  William H. Sandholm,et al.  Population Games And Evolutionary Dynamics , 2010, Economic learning and social evolution.

[3]  David P. Stone.  An Autonomous Agent for Supply Chain Management , 2007 .

[4]  Jianmin Jiang,et al.  A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory , 2015, J. Syst. Softw..

[5]  Jörgen W. Weibull,et al.  Evolutionary Game Theory , 1996 .

[6]  Maytal Saar-Tsechansky,et al.  Editor’s comments: the business of business data science in IS journals , 2015 .

[7]  M. Schaffer,et al.  Evolutionarily stable strategies for a finite population and a variable contest size. , 1988, Journal of theoretical biology.

[8]  D. E. Matthews.  Evolution and the Theory of Games , 1977 .

[9]  Arend Hintze,et al.  Evolutionary game theory using agent-based methods. , 2014, Physics of life reviews.

[10]  Riyaz Sikora,et al.  Learning bidding strategies with autonomous agents in environments with unstable equilibrium , 2008, Decis. Support Syst..

[11]  Josef Hofbauer,et al.  Evolutionary Games and Population Dynamics , 1998 .

[12]  Michael P. Wellman,et al.  Empirical Game-Theoretic Analysis of the TAC Market Games , 2006 .

[13]  Prabhjot Singh,et al.  Adaptive Selection of Cryptographic Protocols in Wireless Sensor Networks using Evolutionary Game Theory , 2016 .

[14]  Thomas Y. Choi,et al.  Supply networks and complex adaptive systems: Control versus emergence , 2001 .

[15]  Riyaz Sikora,et al.  Effect of Reputation Mechanisms and Ratings Biases on Traders’ Behavior in Online Marketplaces , 2014, J. Organ. Comput. Electron. Commer..

[16]  J. M. Smith,et al.  The Logic of Animal Conflict , 1973, Nature.

[17]  Parham Azimi,et al.  Designing of an intelligent self-adaptive model for supply chain ordering management system , 2015, Eng. Appl. Artif. Intell..

[18]  Riyaz Sikora,et al.  Design of Intelligent Agents for Supply Chain Management , 2015, WEB.

[19]  Barin Nag,et al.  An Adaptive Supplier Selection Mechanism in E-Procurement Marketplace , 2017, Journal of International Technology and Information Management.

[20]  Fernando Vega-Redondo,et al.  Evolution, Games, and Economic Behaviour , 1996 .

[21]  Riyaz Sikora,et al.  Performance of online reputation mechanisms under the influence of different types of biases , 2014, Inf. Syst. E Bus. Manag..

[22]  Bo An,et al.  A simulation framework for measuring robustness of incentive mechanisms and its implementation in reputation systems , 2015, Autonomous Agents and Multi-Agent Systems.

[23]  R. Amir,et al.  Asset market games of survival: a synthesis of evolutionary and dynamic games , 2010 .

[24]  Samuel A. Ejiaku.  Technology Adoption: Issues and Challenges in Information Technology Adoption in Emerging Economies , 2014, Journal of International Technology and Information Management.

[25]  Brett J. Borghetti,et al.  Performance Evaluation Methods for the Trading Agent Competition , 2006, AAAI.

[26]  R. G. Ingalls,et al.  Agent-Based Modeling and Simulation , 2017, Encyclopedia of Machine Learning and Data Mining.