8,353 research outputs found
Applications of Negotiation Theory to Water Issues
The purpose of the paper is to review the applications of non-cooperative bargaining theory to water related issues – which fall in the category of formal models of negotiation. The ultimate aim is that to, on the one hand, identify the conditions under which agreements are likely to emerge, and their characteristics; and, on the other hand, to support policy makers in devising the “rules of the game” that could help obtain a desired result. Despite the fact that allocation of natural resources, especially of trans-boundary nature, has all the characteristics of a negotiation problem, there are not many applications of formal negotiation theory to the issue. Therefore, this paper first discusses the non-cooperative bargaining models applied to water allocation problems found in the literature. Particular attention will be given to those directly modelling the process of negotiation, although some attempts at finding strategies to maintain the efficient allocation solution will also be illustrated. In addition, this paper will focus on Negotiation Support Systems (NSS), developed to support the process of negotiation. This field of research is still relatively new, however, and NSS have not yet found much use in real life negotiation. The paper will conclude by highlighting the key remaining gaps in the literature.Negotiation theory, Water, Agreeements, Stochasticity, Stakeholders
Mechanism design for distributed task and resource allocation among self-interested agents in virtual organizations
The aggregate power of all resources on the Internet is enormous. The Internet can
be viewed as a massive virtual organization that holds tremendous amounts of information
and resources with different ownerships. However, little is known about how to run this
organization efficiently.
This dissertation studies the problems of distributed task and resource allocation
among self-interested agents in virtual organizations. The developed solutions are not
allocation mechanisms that can be imposed by a centralized designer, but decentralized
interaction mechanisms that provide incentives to self-interested agents to behave
cooperatively. These mechanisms also take computational tractability into consideration
due to the inherent complexity of distributed task and resource allocation problems.
Targeted allocation mechanisms can achieve global task allocation efficiency in a
virtual organization and establish stable resource-sharing communities based on agentsâÃÂÃÂ
own decisions about whether or not to behave cooperatively. This high level goal requires
solving the following problems: synthetic task allocation, decentralized coalition formation
and automated multiparty negotiation. For synthetic task allocation, in which each task needs to be accomplished by a
virtual team composed of self-interested agents from different real organizations, my
approach is to formalize the synthetic task allocation problem as an algorithmic mechanism
design optimization problem. I have developed two approximation mechanisms that I prove
are incentive compatible for a synthetic task allocation problem.
This dissertation also develops a decentralized coalition formation mechanism,
which is based on explicit negotiation among self-interested agents. Each agent makes its
own decisions about whether or not to join a candidate coalition. The resulting coalitions
are stable in the core in terms of coalition rationality. I have applied this mechanism to
form resource sharing coalitions in computational grids and buyer coalitions in electronic
markets.
The developed negotiation mechanism in the decentralized coalition formation
mechanism realizes automated multilateral negotiation among self-interested agents who
have symmetric authority (i.e., no mediator exists and agents are peers).
In combination, the decentralized allocation mechanisms presented in this
dissertation lay a foundation for realizing automated resource management in open and
scalable virtual organizations
Micro-bias and macro-performance
We use agent-based modeling to investigate the effect of conservatism and
partisanship on the efficiency with which large populations solve the density
classification task--a paradigmatic problem for information aggregation and
consensus building. We find that conservative agents enhance the populations'
ability to efficiently solve the density classification task despite large
levels of noise in the system. In contrast, we find that the presence of even a
small fraction of partisans holding the minority position will result in
deadlock or a consensus on an incorrect answer. Our results provide a possible
explanation for the emergence of conservatism and suggest that even low levels
of partisanship can lead to significant social costs.Comment: 11 pages, 5 figure
Social Networks
We survey the literature on social networks by putting together the economics, sociological and physics/applied mathematics approaches, showing their similarities and differences. We expose, in particular, the two main ways of modeling network formation. While the physics/applied mathematics approach is capable of reproducing most observed networks, it does not explain why they emerge. On the contrary, the economics approach is very precise in explaining why networks emerge but does a poor job in matching real-world networks. We also analyze behaviors on networks, which take networks as given and focus on the impact of their structure on individuals’ outcomes. Using a game-theoretical framework, we then compare the results with those obtained in sociology.random graph, game theory, centrality measures, network formation, weak and strong ties
Social Networks
We survey the literature on social networks by putting together the economics, sociological and physics/applied mathematics approaches, showing their similarities and differences. We expose, in particular, the two main ways of modeling network formation. While the physics/applied mathematics approach is capable of reproducing most observed networks, it does not explain why they emerge. On the contrary, the economics approach is very precise in explaining why networks emerge but does a poor job in matching real-world networks. We also analyze behaviors on networks, which take networks as given and focus on the impact of their structure on individuals’ outcomes. Using a game-theoretical framework, we then compare the results with those obtained in sociology.Random Graph; Game Theory; Centrality Measures; Network Formation; Weak
Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture
Multi-agent coordination mechanisms are frequently used in pursuit-evasion games with the aim of enabling the coalitions of the pursuers and unifying their individual skills to deal with the complex tasks encountered. In this paper, we propose a coalition formation algorithm based on organizational principles and applied to the pursuit-evasion problem. In order to allow the alliances of the pursuers in different pursuit groups, we have used the concepts forming an organizational modeling framework known as YAMAM (Yet Another Multi Agent Model). Specifically, we have used the concepts Agent, Role, Task, and Skill, proposed in this model to develop a coalition formation algorithm to allow the optimal task sharing. To control the pursuers\u27 path planning in the environment as well as their internal development during the pursuit, we have used a Reinforcement learning method (Q-learning). Computer simulations reflect the impact of the proposed techniques
Adaptive and learning-based formation control of swarm robots
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation
Networks of Relations
We model networks of relational (or implicit)contracts, exploring how sanctioning power and equilibrium conditions change under different network configurations and information transmission technologies. In our model, relations are the links, and the value of the network lies in its ability to enforce cooperative agreements that could not be sustained if agents had no access to other network members’ sanctioning power and information. We identify conditions for network stability and in-network information transmission as well as conditions under which stable subnetworks inhibit more valuable larger networks.Networks; Relational Contracts; Peering; Indirect Multimarket Contact; Information transmission; Social Capital.
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