An Experimental Study of the Holdout Problem in a Multilateral Bargaining Game

When an economic exchange requires agreement by multiple independent parties, the potential exists for an individual to strategically delay agreement in an attempt to capture a greater share of the surplus created by the exchange. This holdout problem is a common feature of the land-assembly literature because development frequently requires the assembly of multiple parcels of land. We use experimental methods to examine holdout behavior in a laboratory bargaining game that involves multi-person groups, complementary exchanges, and holdout externalities. The results of six treatments that vary the bargaining institution, number of bargaining periods, and cost of delay demonstrate that holdout is common across institutions and is, on average, a payoff-improving strategy for responders. Both proposers and responders take a more aggressive initial bargaining stance in multi-period bargaining treatments relative to single-period treatments, but take a less aggressive bargaining stance when delay is costly. Nearly all exchanges eventually occur in our multi-period treatments, leading to higher overall efficiency relative to the single-period treatments, both with and without delay costs. [excerpt

Cooperative Precoding/Resource Allocation Games under Spectral Mask and Total Power Constraints

The use of orthogonal signaling schemes such as time-, frequency-, or code-division multiplexing (T-, F-, CDM) in multi-user systems allows for power-efficient simple receivers. It is shown in this paper that by using orthogonal signaling on frequency selective fading channels, the cooperative Nash bargaining (NB)-based precoding games for multi-user systems, which aim at maximizing the information rates of all users, are simplified to the corresponding cooperative resource allocation games. The latter provides additional practically desired simplifications to transmitter design and significantly reduces the overhead during user cooperation. The complexity of the corresponding precoding/resource allocation games, however, depends on the constraints imposed on the users. If only spectral mask constraints are present, the corresponding cooperative NB problem can be formulated as a convex optimization problem and solved efficiently in a distributed manner using dual decomposition based algorithm. However, the NB problem is non-convex if total power constraints are also imposed on the users. In this case, the complexity associate with finding the NB solution is unacceptably high. Therefore, the multi-user systems are categorized into bandwidth- and power-dominant based on a bottleneck resource, and different manners of cooperation are developed for each type of systems for the case of two-users. Such classification guarantees that the solution obtained in each case is Pareto-optimal and actually can be identical to the optimal solution, while the complexity is significantly reduced. Simulation results demonstrate the efficiency of the proposed cooperative precoding/resource allocation strategies and the reduced complexity of the proposed algorithms.Comment: 33 pages, 8 figures, Submitted to the IEEE Trans. Signal Processing in Oct. 200

Demand commitment bargaining: The case of apex games

Game Theory

Bargaining Multiple Issues with Leximin Preferences

Global bargaining problems over a finite number of different issues, are formalized as cartesian products of classical bargaining problems. For maximin and leximin bargainers we characterize global bargaining solutions that are efficient and satisfy the requirement that bargaining separately or globally leads to equivalent outcomes. Global solutions in this class are constructed from the family of monotone path solutions for classical bargaining problems.Global bargaining, maximin preferences, leximin preferences

A Multi-Agent Systems Approach to Microeconomic Foundations of Macro

This paper is part of a broader project that attempts to gener- ate microfoundations for macroeconomics as an emergent property of complex systems. The multi-agents systems approach is seen to produce realistic macro properties from a primitive set of agents that search for satisfactory activities, "jobs", in an informationally constrained, computationally noisy environment. There is frictional and structural unemployment, inflation, excess capacity, fi- nancial instability along with the possibility of relatively smooth expansion. There is no Phillips curve but an inegalitarian distribution of income emerges as fundamental property of the system.Multi-agent system, agent-based models, microeconomic foundations, macroeconomics.

Learning to Reach Agreement in a Continuous Ultimatum Game

It is well-known that acting in an individually rational manner, according to the principles of classical game theory, may lead to sub-optimal solutions in a class of problems named social dilemmas. In contrast, humans generally do not have much difficulty with social dilemmas, as they are able to balance personal benefit and group benefit. As agents in multi-agent systems are regularly confronted with social dilemmas, for instance in tasks such as resource allocation, these agents may benefit from the inclusion of mechanisms thought to facilitate human fairness. Although many of such mechanisms have already been implemented in a multi-agent systems context, their application is usually limited to rather abstract social dilemmas with a discrete set of available strategies (usually two). Given that many real-world examples of social dilemmas are actually continuous in nature, we extend this previous work to more general dilemmas, in which agents operate in a continuous strategy space. The social dilemma under study here is the well-known Ultimatum Game, in which an optimal solution is achieved if agents agree on a common strategy. We investigate whether a scale-free interaction network facilitates agents to reach agreement, especially in the presence of fixed-strategy agents that represent a desired (e.g. human) outcome. Moreover, we study the influence of rewiring in the interaction network. The agents are equipped with continuous-action learning automata and play a large number of random pairwise games in order to establish a common strategy. From our experiments, we may conclude that results obtained in discrete-strategy games can be generalized to continuous-strategy games to a certain extent: a scale-free interaction network structure allows agents to achieve agreement on a common strategy, and rewiring in the interaction network greatly enhances the agents ability to reach agreement. However, it also becomes clear that some alternative mechanisms, such as reputation and volunteering, have many subtleties involved and do not have convincing beneficial effects in the continuous case