Bargaining with posterior opportunities: an evolutionary social simulation

Negotiations have been extensively studied theoretically throughout the years. A well-known bilateral approach is the ultimatum game, where two agents negotiate on how to split a pie or a `dollar': the proposer makes an offer and responder can choose to accept or reject. In this paper a natural extension of the ultimatum game is presented, in which both agents can negotiate with other opponents in case of a disagreement. This way the basics of a competitive market are modelled where for instance a buyer can try several sellers before making a purchase decision. The game is investigated using an evolutionary simulation. The outcomes appear to depend largely on the information available to the agents. We find that if the agents' number of future bargaining opportunities is commonly known, the proposer has the advantage. If this information is held private, however, the responder can obtain a larger share of the pie. For the first case we also provide a game-theoretic analysis and compare the outcome with evolutionary results. Furthermore, the effects of search costs and allowing multiple issues to be negotiated simultaneously are investigated

Equilibrium selection in alternating-offers bargaining models: the evolutionary computing approach

A systematic validation of evolutionary techniques in the field of bargaining is presented. For this purpose, the dynamic and equilibrium-selecting behavior of a multi-agent system consisting of adaptive bargaining agents is investigated. The agents' bargaining strategies are updated by an evolutionary algorithm (EA), an innovative computational method to simulate collective learnin g in societies of boundedly-rational agents. Negotiations between the agents are governed by the well-known``alternating-offers' protocol. Using this protocol, the influence of various important factors (like the finite length of the game, time preferences, exogenous breakdown, and risk aversiveness) is investigated. We show that game theory can be used successfully to interpret the equilibrium-selecting behavior observed in computational experiments with adaptive bargaining agents. Agreement between theory and experiment is especially good when the agents experience an intermediate time pressure. Deviations from classical game theory are, however, observed in several experiments. Violent nonlinear oscillations may for instance occur in the single-stage ultimatum game. We demonstrate that the specific evolutionary model governing agent selection is an important factor under these conditions. In multiple-stage games, the evolving agents do not always fully perceive and exploit the finite horizon of the game (even when time pressure is weak). This effect can be attributed to the boundedly-rational behavior of the adapting agents. Furthermore, when the agents discount their payoffs at a different rate, the agent with the largest discount factor is not able to exploit his bargaining power completely, being under pressure by his impatient opponent to reach an early agreement. Negotiations over multiple issues, a particularly important aspect of electronic trading, are studied in a companion paper cite{Gerding:00. We are currently investigating the behavior of more complex and powerful bargaining agents

Competitive market-based allocation of consumer attention space

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding-strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains

Multi-attribute bilateral bargaining in a one-to-many setting

Negotiations are an important way of reaching agreements between selfish autonomous agents. In this paper we focus on one-to-many bargaining within the context of agent-mediated electronic commerce. We consider an approach where a seller negotiates over multiple interdependent attributes with many buyers individually. Bargaining is conducted in a bilateral fashion, using an alternating-offers protocol. In such a one-to-many setting, “fairness,” which corresponds to the notion of envy-freeness in auctions, may be an important business constraint. For the case of virtually unlimited supply (such as information goods), we present a number of one-to-many bargaining strategies for the seller, which take into account the fairness constraint, and consider multiple attributes simultaneously. We compare the performance of the bargaining strategies using an evolutionary simulation, especially for the case of impatient buyers and small premature bargaining break off probability. Several of the developed strategies are able to extract almost all the surplus; they utilize the fact that the setting is one-to-many, even though bargaining occurs in a bilateral fashion

Managing Linguistic Data Summaries in Advanced P2P Applications

chapitre... à corrigerAs the amount of stored data increases, data localization techniques become no longer sufficient in P2P systems. A practical approach is to rely on compact database summaries rather than raw database records, whose access is costly in large P2P systems. In this chapter, we describe a solution for managing linguistic data summaries in advanced P2P applications which are dealing with semantically rich data. The produced summaries are synthetic, multidimensional views over relational tables. The novelty of this proposal relies on the double summary exploitation in distributed P2P systems. First, as semantic indexes, they support locating relevant nodes based on their data descriptions. Second, due to their intelligibility, these summaries can be directly queried and thus approximately answer a query without the need for exploring original data. The proposed solution consists first in defining a summary model for hierarchical P2P systems. Second, appropriate algorithms for summary creation and maintenance are presented. A query processing mechanism, which relies on summary querying, is then proposed to demonstrate the benefits that might be obtained from summary exploitation

Numerical Test of Disk Trial Wave function for Half-Filled Landau Level

The analyticity of the lowest Landau level wave functions and the relation between filling factor and the total angular momentum severely limits the possible forms of trial wave functions of a disk of electrons subject to a strong perpendicular magnetic field. For N, the number of electrons, up to 12 we have tested these disk trial wave functions for the half filled Landau level using Monte Carlo and exact diagonalization methods. The agreement between the results for the occupation numbers and ground state energies obtained from these two methods is excellent. We have also compared the profile of the occupation number near the edge with that obtained from a field-theoretical method. The results give qualitatively identical edge profiles. Experimental consequences are briefly discussed.Comment: To be published in Phys. Rev. B. 9 pages, 6 figure

Characterisation and modelling of the plastic material behaviour and its application in sheet metal forming simulation

The application of simulation models in sheet metal forming in automotive industry has proven to be beneficial to reduce tool costs in the designing stage and for optimising current processes. Moreover, it is a promising tool for a material supplier to optimise material choice and development for both its final application and its forming capacity. The present practice requires a high predictive value of these simulations. The material models in these simulation models need to be developed sufficiently to meet the requirement of the predictions. For the determination of parameters for the material models, mechanical tests at different strain paths are necessary 1. Usually, the material models implemented in the simulation models are not able to describe the plastic material behaviour during monotonic strain paths sufficiently accurate 2. This is true for the strain hardening model, the influence of strain rate and the description of the yield locus in these models. A first stage is to implement the improved material models which describe this single strain path behaviour in a better way. In this work, different yield criteria, a hardening model and their comparison to experiments are described extensively. The improved material model has been validated initially on forming limit curves which are determined experimentally with Nakazima strips. These results will be compared with predictions using Marciniak-Kuczinsky-analysis with both the new material model and the conventional material model. Finally, the validation on real pressed products will be shown by comparing simulation results using different material models with the experimental data. The next challenge is the description of the material after a change of strain path. Experimental evidence given here shows that this behaviour cannot be treated using the classical approach of an equivalent strain as the only history variable

Single Top Production as a Window to Physics Beyond the Standard Model

Production of single top quarks at a high energy hadron collider is studied as a means to identify physics beyond the standard model related to the electroweak symmetry breaking. The sensitivity of the $s$-channel $W^*$ mode, the $t$-channel $W$-gluon fusion mode, and the \tw mode to various possible forms of new physics is assessed, and it is found that the three modes are sensitive to different forms of new physics, indicating that they provide complimentary information about the properties of the top quark. Polarization observables are also considered, and found to provide potentially useful information about the structure of the interactions of top.Comment: References added and minor discussion improvements; results unchanged; Version to be published in PR

The fully differential single-top-quark cross section in next-to-leading order QCD

We present a new next-to-leading order calculation for fully differential single-top-quark final states. The calculation is performed using phase space slicing and dipole subtraction methods. The results of the methods are found to be in agreement. The dipole subtraction method calculation retains the full spin dependence of the final state particles. We show a few numerical results to illustrate the utility and consistency of the resulting computer implementations.Comment: 37 pages, latex, 2 ps figure