Papers

K. Ehrhart, C. Keser

In public goods experiments where subjects may change groups, we observe a continual flight of the more cooperative subjects away from the less cooperative ones. The less cooperative subjects attempt to enter cooperative groups in order to free-ride on their contributions.

In the symmetric n-player public good game, each of the n players is endowed with 10 tokens to be allocated between two alternatives, called A and B. Alternative A is private: each token allocated to alternative A yields the player a return of 1. Alternative B is public: each token allocated to alternative B by any of the n players yields each player in the group an individual return of k(n), that depends on the group size n. The individual return per token k(n) is smaller than 1, the return per token in the private alternative A. However, the return per token to the entire group r(n) = nk(n) is greater than 1 (for n > 1). contribution to the private alternative maximizes one’s individual profit but leads to an undesirable outcome for the group.

Each player decides whether he wants to stay with his group, switch to one of the other groups that existed in the previous round, or create a new group.

Experiments with 90 students that know all the rules of the game, but cannot communicate. The major conclusion that we can draw out of these results is that both free-riding and cooperating are active principles.

A. Rodrigues, 1997

The concept of agent is becoming increasingly important not only in research (where it has been in use for some time) but also now in commercial applications. However, an agent may represent many d ifferent things according to the people that implement and use them. Based on the concept of agent we define Spatial Agents as agents that make spatial concepts computable. By implementing spatial agents we hope to solve the following problems : Locating and retrieving Spatial Information in large networks (and specifically the Internet), Facilitate the handling of a GIS user interface, Implementing improved spatial tasks and Creating interfaces between GIS and specific software packages. We discuss what are the necessary qualities that a development tool should have to qualify for agent development. We select some of the most prominent tools currently used and try to choose which are best suited for the development of spatial agents. Finally, we reflect on the design of spatial agents that will solve the problems mentioned above and present a prototype of an Interface Agent for the Drawing tool of the Smallworld GIS.

T. P. Evans and W. Sun and H. Kelley

We explore the special outcomes of decision-making through two laboratorybased experiments, one with a homogenous land suitability surface and another with a heterogeneous suitability surface. Subjects make resource allocation decisions on an abstract landscape and are given a monetary incentive to maximize their revenue during the experiment. These experimental results are compared with simulation output from an agent-based model run on the same abstract landscape that uses a utility-maximizing agent. The main findings are: (1) landscapes produced by subjects result in greater patchiness and more edge than the utility-maximization agent predicts; (2) there is considerable diversity in the decisions subjects make despite the relatively simple decision-making context; and (3) there is greater deviation of subject revenue from the maximum potential revenue in early rounds of the experiment compared with later rounds, demonstrating the challenge of making optimal decisions with little historical context. The findings demonstrate the value of using non-maximizing agents in agent-based models of land-cover change and the importance of acknowledging actor heterogeneity in land-change systems.

We use the term 'experiment' to refer to a laboratory experiment where human subjects are faced with a specific decision-making task rather than to refer to a model run or simulation.

Game theoretic approaches present simplified decision-making settings that are then, with caution, extrapolated to real-world contexts.

They cite some works of games, but they are 'RPG', instead of 'experiments'.

C.D. Aliprantis and G. Camera and D. Puzzellob, 2006

We develop theoretical underpinnings of pairwise random matching processes. We formalize the mechanics of matching, and study the links between properties of the different processes and trade frictions. A particular emphasis is placed on providing a mapping between matching technologies and informational constraints.

The technical procedure that we use to construct any random matching process involves three basic steps. The first step is to specify how to divide the population in each period into spatially separated clusters of agents. To do so, we use partitional correspondences. Then, one must define and calculate all possible ways to form pairs in each cluster. In this case, we resort to using a class of permutation functions, the so-called involutions. Finally, for each period one must specify a probability measure over all possible pairings, for each cluster.

Provides a formal definition for spatial clusters. Definition of bilateral matching.

(the population has infinitely many agents) That is, not only every pair meets again with probability zero (which we called weak anonymity), but also we show how to ensure that every pair does not share past partners, etc. In brief, we construct matches in which agents are completely isolated from an informational standpoint.

Qin Yong Wang YingJie Jia Li-Min

They present an implementation, with little details, and show the traffic in China railways along common days and on holidays. The paper quality is bad, and I cannot see the details of the automata.