Alternatingoffers Bargaining problems A Coevolutionary Approach Department of

Alternating-offers Bargaining problems A Co-evolutionary Approach Department of Computer Science Nanlin Jin, Professor Edward Tsang, Professor Abhinay Muthoo, Tim Gosling, Dr Maria Fasli, Dr Sheri Markose, Guannan Wang http: //cswww. essex. ac. uk/Research/CSP/bargain Basic Alternating-Offers Bargaining Problem Bargaining theory studies a class of bargaining situations where two players have common interests, usually called “cake”, but conflict over how the cake is divided. Under the “No delay” and “Stationarity” assumptions, Perfect Equilibrium Partition (P. E. P) of the basic Alternating-Offer Bargaining is: X* Where A and X*B are the optimal share for A and B, respectively, A and B are their discount factors Evolutionary Computation Evolution Computation, inspired by nature, has been proved successful in studying adaptive systems. It is especially good for non-linear, epistatic, large searchspace problems. Evolution Process: Ø A set of candidate solutions is called a “population”; Ø Survival of fittest: the better performance, the higher possibility to be selected as parents of the next generation; Ø Crossover and Mutation: modifications used to generate the next generation. In situations when we are unable to compute the P. E. P. , can we evolve sensible bargaining strategies? Technical Overview Observations Computer Scientists More Realistic Assumptions i. Players are allowed to take any division of the cake, if share xi (0, 1]; ii. Players have neither the knowledge of P. E. P nor the intelligent reasoning ability as economists. But players have the basic common senses, that are: the higher payoff the better, and the higher bargaining cost the lower payoff. iii. One player doesn’t know the other’s behaviours before bargaining starts; => bounded rationality Co-evolutionary System For the bargaining problem, co-evolution is required as (a) the fitness is assessed by bargaining outcomes between strategies from co-evolving populations; and (b) the two players may have different information. In Biology, co-evolution is defined as reciprocal evolutionary change in interacting species. People A Generation 0 B -Generation 0 A Generation 1 B -Generation 1 A Generation 2 B -Generation 2 A Generation n B -Generation n Edward Tsang Computational Finance & Economics Co-adaptive Learning: Strategies modify in beneficial ways to adapt to dynamic environments through reinforcement ‘learning’. Usually both players’ behaviours and bargaining outcomes stabilize near to P. E. P after a sufficiently long leaning period. Maria Fasli TAC Auction Nanlin Jin Tim Gosling Evolutionary Distributed Bargaining Theory Constraint satisfaction Observations Contact Cake Partitions by Co-Evolution: For more information, visit: In general, co-evolutionary system can find out approximate solutions with low cost and reasonable time. Experimental agreements distribute within the P. E. P neighbourhood. Guannan Wang Bargaining Software Economists Abhinay Muthoo Expert in Bargaining Theory Run time: 100 runs last for only about 1 or 2 days Riccardo Poli Genetic Programming Sheri Markose Director CCFEA Conclusions Computational Finance: http: //cswww. essex. ac. uk/Research/CSP/finance Ø Strategies do ‘learn’ to perform better during coevolution process. Center for Computation Finance and Economic Agents: (CCFEA) http: //www. cfea-labs. net Ø And, experienced players make more efficient agreements with less money left on table. For possible collaboration, contact: Ø Excluding situations with extreme discount factors, coevolution processes converged to bargaining agreements that cluster around P. E. P, even under much weak assumptions. Professor Edward Tsang Phone: +44 1206 872774; email: edward@essex. ac. uk Nanlin Jin Phone: +44 1206 872771; email: njin@essex. ac. uk Ø Co-evolution can be regarded as an effective complementary and approximate method to the economics theoretical approach. Funding Ø This framework is ready for us to study more complex bargaining problems with few modifications. This research has been partly funded by BT and University of Essex