Nanlin Jin

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— Bargaining is a fundamental activity in social life. Game-theoretic methodology has provided perfect solutions for certain abstract models. Even for a simple model, this method demands substantial human intelligent effort in order to solve game-theoretic equilibriums. The analytic complexity increases rapidly when more elements are included in the models.(More)
Axelrod's originally experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that while PBIL performs well, GA in general does slightly better although more experiments should be(More)
The main purpose of this work is to measure the effect of bargaining players' information completeness on agreements in evolutionary environments. We apply Co-evolutionary Algorithms to solve four incomplete information bargaining problems. Empirical analyses indicate that without complete information of player(s)'s preferences, co-evolving populations are(More)
Anticipating and managing future trade-offs and complementarities between ecosystem services. ABSTRACT. This paper shows how, with the aid of computer models developed in close collaboration with decision makers and other stakeholders, it is possible to quantify and map how policy decisions are likely to affect multiple ecosystem services in future. In this(More)
1. Introduction Many economic problems, such as bargaining, financial investment, supply-chain management, vehicle routing and time-tabling can be treated as optimization problems with certain constraints associated. Optimization entails attempting to find the ''best solution'' among all possible solutions, where the ''best'' is always subject to various(More)
In this paper, we apply an Evolutionary Algorithm (EA) to solve the Rubinstein's Basic Alternating-Offer Bargaining Problem, and compare our experimental results with its analytic game-theoretic solution. The application of EA employs an alternative set of assumptions on the players' behaviors. Experimental outcomes suggest that the applied co-evolutionary(More)
— This paper aims to forecast the economic impacts of changing land-use in UK uplands. We assume that farmers adaptively learn and respond to a dynamic economic environment. The main research approach is the use of evolutionary algorithms for dynamic optimization. We use this approach to study how the changes of agricultural subsidy policy (CAP reform)(More)
The use of evolutionary computation is significant for the development and optimisation of strategies for dynamic and uncertain situations. This chapter introduces three cases in which evolutionary computation has already been used successfully for strategy generation in the form of work on the Iterated Prisoner's Dilemma, Rubinstein's alternating offers(More)