Keywords. Agent-based systems, market game, multi-agent simulation, procurement, intelligent agents, supply chain management, trading agents. Supply chain management deals with the planning and coordination of bidding, production, sourcing and procurement activities associated with one or more products. It is central to today’s global economy, leading to trillions of dollars in annual transactions worldwide. With the emergence of electronic marketplaces, it is only natural to seek automated solutions that are capable of rapidly evaluating a large number of bidding, sourcing and procurement options. In this paper, we detail a game we have designed to promote the research and evaluation of such solutions under realistic conditions. The game requires agents to manage the assembly of PCs, while competing with one another both for customer orders and for key components. We discuss how the game captures the complexity, stochasticity and competitive nature inherent to supply chain environments. A Web-based multi-agent simulation platform developed for the game was implemented in 2003 and validated in the context of the first Supply Chain Management Trading Agent Competition (TAC-SCM). A total of 20 teams from around the world competed with one another. We review agent strategies developed by different teams and discuss the merits of competition-based research over more traditional research methodologies in this area. Page 1

We examine the force of three types of behavioral dynamics in quantity-setting triopoly experiments: (1) mimicking the successful firm, (2) rules based on following the exemplary firm, and (3) rules based on belief learning. Theoretically, these three types of rules lead to the competitive, the collusive, and the CournotNash outcome, respectively. In the experiment we employ three information treatments, each of which is hypothesized to be conducive to the force of one of the three dynamic rules. To a large extent, the results are consistent with the hypothesized relationships between treatments, behavioral rules, and outcomes.

This paper studies decision-making with rules of thumb in the context of dynamic decision problems and compares it to dynamic programming. A rule is a fixed mapping from a subset of states into actions. Rules are compared by averaging over past experiences. This can lead to favoring rules which are only applicable in good states. Correcting this good state bias requires solving the dynamic program. We provide a general framework and characterize the asymptotic properties. We apply it to provide a candidate explanation for the sensitivity of consumption to transitory income.

In general, identifying a solution concept only incompletely specifies a mechanism design problem. The designer must consider which among a multiplicity of solutions is likely to be played, as well as the possibility that actual play will not correspond to any solution. Given that actual play is the ultimate determiner of a mechanism’s success, we advocate that designers embrace the corresponding forecasting problem and evaluate candidate mechanisms with respect to belief distributions over players ’ response. Solution concepts can play a useful role in delimiting and structuring belief distributions. We propose that membership of prospective strategy profiles in various solution classes be treated as evidence bearing on their likelihood of play. Flexible solution classes, for example based on approximate equilibrium, degree of dominance, or safety level, provide natural measures (e.g., distance from equilibrium) that can be employed in defining belief distributions. 1.

Abstract not found

This paper sketches a framework for the analysis of expectations in an evolutionary microeconomics. The core proposition is that expectations form a network structure, and that the geometry of that network will provide a suitable guide as to the dynamical behaviour of that network. It is a development towards a theory of the computational processes that construct the data set of expectations. The role of probability theory is examined in this context. Two key issues will be explored: (1) on the nature and stability of expectations when they form as a complex network; and (2), the way in which this may be modelled within a multi-agent simulation platform. It is argued that multi-agent simulation (a-life) techniques provide an expedient analytical environment to study the dynamic nature of mass expectations, as generated or produced objects, in a way that bridges micro and macroeconomics.

intelligent agents, supply chain management, trading agents. Supply chain management deals with the planning and coordination of bidding, production, sourcing and procurement activities associated with one or more products. It is central to today’s global economy, leading to trillions of dollars in annual transactions worldwide. With the emergence of electronic marketplaces, it is only natural to seek automated solutions that are capable of rapidly evaluating a large number of bidding, sourcing and procurement options. In this paper, we detail a game we have designed to promote the research and evaluation of such solutions under realistic conditions. The game requires agents to manage the assembly of PCs, while competing with one another both for customer orders and for key components. We discuss how the game captures the complexity, stochasticity and competitive nature inherent to supply chain environments. A Web-based multi-agent simulation platform developed for the game was implemented in 2003 and validated in the context of the first Supply Chain Management Trading Agent Competition (TAC-SCM). A total of 20 teams from around the world competed with one another. We review agent strategies developed by different teams and discuss the merits of competition-based research over more traditional research methodologies in this area. Page 3

Preliminary and incomplete. Comments are welcome.

Simultaneous ascending auctions present agents with various strategic problems, depending on preference structure. As long as bids represent non-repudiable offers, submitting noncontingent bids to separate auctions entails an exposure problem: bidding to acquire a bundle risks the possibility of obtaining an undesired subset of the goods. With multiple goods (or units of a homogeneous good) bidders also need to account for their own effects on prices. Auction theory does not provide analytic solutions for optimal bidding strategies in the face of these problems. We present a new family of decision-theoretic bidding strategies that use probabilistic predictions of final prices: self-confirming distribution-prediction strategies. Bidding based on these is provably not optimal in general. But evidence using empirical game-theoretic methods we developed indicates the strategy is quite effective compared to other known methods when preferences exhibit complementarities. When preferences exhibit substitutability, simpler demand-reduction strategies address the own price effect problem more directly and perform better.