Coordination of Decisions in a Spatial Agent Model

For a binary choice problem, the spatial coordination of decisions in an agent community is investigated both analytically and by means of stochastic computer simulations. The individual decisions are based on different local information generated by the agents with a finite lifetime and disseminated in the system with a finite velocity. We derive critical parameters for the emergence of minorities and majorities of agents making opposite decisions and investigate their spatial organization. We find that dependent on two essential parameters describing the local impact and the spatial dissemination of information, either a definite stable minority/majority relation (single-attractor regime) or a broad range of possible values (multi-attractor regime) occurs. In the latter case, the outcome of the decision process becomes rather diverse and hard to predict, both with respect to the share of the majority and their spatial distribution. We further investigate how a dissemination of information on different time scales affects the outcome of the decision process. We find that a more ``efficient'' information exchange within a subpopulation provides a suitable way to stabilize their majority status and to reduce ``diversity'' and uncertainty in the decision process.Comment: submitted for publication in Physica A (31 pages incl. 17 multi-part figures

Technic and Collaboration Breakdown Structures: Drivers of collaborative problem solving approaches in a supply chain context

Problem Solving Methodologies have been par excellence a cornerstone element of the firms’ strategy on achieving effective continuous improvement. But the enterprise evolution towards an extended environment characterized by network-based organization has radically changed the problem solving paradigms. This paper aims to propose a generic and collaborative methodology addressing more complex and distributed problems, dealing with Supply Chain issues and having a key role as a driver for building global competitive advantages and create superior performances at a Supply Chain level

Distributed agent-based building evacuation simulator

The optimisation of the evacuation of a building plays a fundamental role in emergency situations. The behaviour of individuals, the directions that civilians receive, and the actions of the emergency personnel, will affect the success of the operation. We describe a simulation system that represents the individual, intelligent, and interacting agents that cooperate and compete while evacuating the building. The system also takes into account detailed information about the building and the sensory capabilities that it may contain. Since the level of detail represented in such a simulation can lead to computational needs that grow at least as a polynomial function of the number of the simulated agents, we propose an agent-oriented Distributed Building Evacuation Simulator (DBES). The DBES is integrated with a wireless sensor network which offers a closed loop representation of the evacuation procedure, including the sensed data and the emergency decision making

Decoding the neural substrates of reward-related decision making with functional MRI

Although previous studies have implicated a diverse set of brain regions in reward-related decision making, it is not yet known which of these regions contain information that directly reflects a decision. Here, we measured brain activity using functional MRI in a group of subjects while they performed a simple reward-based decision-making task: probabilistic reversal-learning. We recorded brain activity from nine distinct regions of interest previously implicated in decision making and separated out local spatially distributed signals in each region from global differences in signal. Using a multivariate analysis approach, we determined the extent to which global and local signals could be used to decode subjects' subsequent behavioral choice, based on their brain activity on the preceding trial. We found that subjects' decisions could be decoded to a high level of accuracy on the basis of both local and global signals even before they were required to make a choice, and even before they knew which physical action would be required. Furthermore, the combined signals from three specific brain areas (anterior cingulate cortex, medial prefrontal cortex, and ventral striatum) were found to provide all of the information sufficient to decode subjects' decisions out of all of the regions we studied. These findings implicate a specific network of regions in encoding information relevant to subsequent behavioral choice

Gathering experience in trust-based interactions

As advances in mobile and embedded technologies coupled with progress in adhoc networking fuel the shift towards ubiquitous computing systems it is becoming increasingly clear that security is a major concern. While this is true of all computing paradigms, the characteristics of ubiquitous systems amplify this concern by promoting spontaneous interaction between diverse heterogeneous entities across administrative boundaries [5]. Entities cannot therefore rely on a specific control authority and will have no global view of the state of the system. To facilitate collaboration with unfamiliar counterparts therefore requires that an entity takes a proactive approach to self-protection. We conjecture that trust management is the best way to provide support for such self-protection measures

Models of Consensus for Multiple Agent Systems

Models of consensus are used to manage multiple agent systems in order to choose between different recommendations provided by the system. It is assumed that there is a central agent that solicits recommendations or plans from other agents. That agent the n determines the consensus of the other agents, and chooses the resultant consensus recommendation or plan. Voting schemes such as this have been used in a variety of domains, including air traffic control. This paper uses an analytic model to study the use of consensus in multiple agent systems. The binomial model is used to study the probability that the consensus judgment is correct or incorrect. That basic model is extended to account for both different levels of agent competence and unequal prior odds. The analysis of that model is critical in the investigation of multiple agent systems, since the model leads us to conclude that in some cases consensus judgment is not appropriate. In addition, the results allow us to determine how many agents should be used to develop consensus decisions, which agents should be used to develop consensus decisions and under which conditions the consensus model should be used.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994