Multi-agent decision-making dynamics inspired by honeybees

When choosing between candidate nest sites, a honeybee swarm reliably chooses the most valuable site and even when faced with the choice between near-equal value sites, it makes highly efficient decisions. Value-sensitive decision-making is enabled by a distributed social effort among the honeybees, and it leads to decision-making dynamics of the swarm that are remarkably robust to perturbation and adaptive to change. To explore and generalize these features to other networks, we design distributed multi-agent network dynamics that exhibit a pitchfork bifurcation, ubiquitous in biological models of decision-making. Using tools of nonlinear dynamics we show how the designed agent-based dynamics recover the high performing value-sensitive decision-making of the honeybees and rigorously connect investigation of mechanisms of animal group decision-making to systematic, bio-inspired control of multi-agent network systems. We further present a distributed adaptive bifurcation control law and prove how it enhances the network decision-making performance beyond that observed in swarms

A multi-agent system to support location based group decision making in mobile teams

This paper describes an agent-based approach for developing a location-based asynchronous group decision-support system for mobile teams. The approach maximises the use of reusable service components (GSCmas — generic service component for multi-agent systems) as the main interaction mechanism between agents to allow flexible support of a new group-decision process. The paper describes the architecture of a GSCmas and provides details of how the GSCmas is integrated within a decision support system. Finally a system (mPower) based on the proposed approach is introduced and applied to a location-based group decision problem

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making

Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Development of a Negotiation Support Model for Value Management in Construction

Decision making for value-based design in Value Management (VM) is very complicated due to the involvement of many parties. In such situation where the designer, project manager, facility manager and others are involved in choosing a single alternative from a set of solutions, a negotiation support is required to evaluate and rank the solution before engaging into negotiation. This research presents a conceptual model of negotiation support for VM. It consists of developing the appropriate research approach, methodology of negotiation and agent-based negotiation in VM. The research objectives are to find a theoretical basis and research approach for negotiation support methodology on VM, to develop a decision model for technical solution options in a satisfying function/cost preferences, to investigate negotiation style and outcome and analyze the correlation between them for the basis of scenarios on the agent system, to develop a model for agreement options and coalition algorithms on value-based decision, to validate the coalition algorithms and introduce an initial model of Negotiation Support for Value Management (NSVM). A triangulation methodology has been used to fulfill the objectives. It combines simultaneous triangulation by using case studies and survey methods and sequential triangulation in which results of one method are essential in planning the next method (theoretical mapping, survey research, focus group, case study and conceptual modeling). The methodology is based on a theoretical approach which consists of value-based decision nature in construction VM, multicriteria group decision making, game theory, negotiation theory, and agent-based development. This methodology combines value analysis method using Function Analysis System Technique (FAST); Life Cycle Cost analysis, group decision analysis method based on Analytical Hierarchy Process (AHP), and Game theory-based agent system to develop a negotiation support

GDSS (Group Decision Support System): Penentuan Passing Grade dengan Menggunakan Metode Utility – based Agent dan DELPHI (Studi Kasus: Penerimaan Mahasiswa Baru STT Telkom) GDSS ( Group Decision Support System ): Determining of Passing Grade uses Utility

ABSTRAKSI: Group Decision Support System merupakan suatu fasilitas pengambilan keputusan berbasis komputer dan jaringan dalam sebuah kelompok untuk memperoleh suatu pertimbangan keputusan bersama. Di dalam Group Decision Support System, proses pengambilan keputusan dapat dianalogikan sebagai sebuah pertemuan kelompok yang sedang melakukan pengambilan suara terbanyak untuk menghasilkan sebuah keputusan bersama. Melalui Group Decision Support System ini, keterbatasan jarak dan waktu dalam menganalisa dan mengambil keputusan lebih dapat teratasi. Mengikuti tradisi sebelumnya ataupun tradisi perguruan tinggi - perguruan tinggi lain, penentuan standar passing grade dalam tes Penerimaan Mahasiswa Baru di Sekolah Tinggi Teknologi Telkom masih dilakukan dengan pertimbangan yang tidak jelas dari pihak dewan jurusan masing-masing. Dampaknya, para peserta tes mengerjakan tes mereka tanpa suatu target nilai tertentu, sehingga mereka juga tidak memiliki strategi yang cukup jelas dalam mengerjakan tes-nya. Analisa dan implementasi aplikasi sistem pendukung pengambilan keputusan yang dikembangkan ini bertujuan untuk membantu rapat dewan dalam menentukan passing grade berdasarkan analisa kemungkinan atau prediksi kualitas dengan menggunakan metode utility – based agent dan DELPHI. Keputusan passing grade dapat dihasilkan secara real time bagi pihak dewan, dapat memberikan target nilai yang jelas bagi para peserta ujian menuju standar kelulusan tes tersebut, maupun dapat memberikan kriteria kelulusan yang dapat dipertanggungjawabkan.Kata Kunci : Group Decision Support System, passing grade, utility – based agent, DELPHI, dan real time.ABSTRACT: Group Decision Support System representing a decision making facility based on computer and network in a group to obtain a consideration of decision. In Group Decision Support System, decision-making processes earn analogy like a group of meeting which in pursuance of voting to a decision with. Through this Group Decision Support System, limitation of time and distance in analysing and taking decision can be more overcame. Following previous tradition or other universities‟ tradition, determination of standard elegibility of passing grade in New Selection Student for University at Telkom School of Engineering still be done with ill defined consideration of each majors council side. Its impact, all testees do their test without a certain value goals; and so they do not have clear strategy in doing their test. These analysis and implementation of application for decision support system which has being developed aim to assist council meeting in determining correct passing grade pursuant to possibility analysis or best quality prediction by using method of utility – based agent and of DELPHI. Decision of passing grade can be released on real time to council side, it can give clear value goals to all testees as a standard passing grade of the test, and also can give criterion pass of which can be justified.Keyword: Group Decision Support System, passing grade, utility – based agent, DELPHI, and real time

Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations

Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theoretic approach to collective decision making, agent-based simulations were conducted to investigate how human collective decision making would be affected by the agents' diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing non-trivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multi-level decision making are discussed.Comment: 27 pages, 5 figures, 2 tables; accepted for publication in Complexit

Simulating the Effect of Social Influence on Decision-Making in Small, Task-Oriented, Groups

This paper describes a simulation study of decision-making. It is based on a model of social influence in small, task-oriented, groups. A process model of dyadic social influence is built on top of a dynamic model of status and task participation that describes the emergence of a stable power and prestige order. Two models of group decision-making are examined: a static model for which the beliefs of actors do not change, and a process model for which they do as a function of the standing of each member of each interacting pair in the evolving power and prestige order. The models are compared on a set of N=111 cases, each requiring an affirmative or negative group response to a proposition A(c) that pertains to a case c. Initial beliefs are assigned to each of five members of distinct professions based on an analysis of independently collected behavioral data pertinent to the proposition to be affirmed or denied in each case. Although the two influence models yield identical decisions in 70% of the cases examined, the differences between them are statistically significant and in several instances show a medium effect size. Most importantly, the differences can be explained in terms of social influence and the status and task participation model on which it depends.Social Influence; Decision Processes; Social Networks; Group Dynamics; Simulation; Agent-Based Modeling

Virtual Medical Board: A Distributed Bayesian Agent Based Approach

Distributed Decision Making has become of increasing importance to get solution of different real life problems, where decision makers are in geographically dispersed locations. Application of agent and multi agent system in this Distributed Decision Support System is an evolving paradigm. One of such real life problem is medical diagnosis. For critical medical diagnosis, medical board is formed which is a coordinative discussion mechanism between a group of expert physicians to diagnose a patient. But always forming a medical board with a group of expert physicians may not be possible due to lack of infrastructure, availability, time etc. In that situation the role of multi agent based distributed decision making can comes into play. In this paper we develop a Virtual Medical Board System in which a number of software agents (expert agents) act as a group of expert physician with knowledge base(KB), reasoning capability. They coordinatively discuss with each other to diagnose a patieh each other to diagnose a patient. We represent the discussion module of the system in the form of Bayesian Network of Bayesian Agent (BNBA). In BNBA each BA is the expert software agent whose Knowledge Base (KB) is represented in the form of Bayesian Network (BN). Also the BDI (Belief, Desire, Intention) model of each BA is represented in this paper