Integrative Use of Information Extraction, Semantic Matchmaking and Adaptive Coupling Techniques in Support of Distributed Information Processing and Decision-Making

In order to press maximal cognitive benefit from their social, technological and informational environments, military coalitions need to understand how best to exploit available information assets as well as how best to organize their socially-distributed information processing activities. The International Technology Alliance (ITA) program is beginning to address the challenges associated with enhanced cognition in military coalition environments by integrating a variety of research and development efforts. In particular, research in one component of the ITA ('Project 4: Shared Understanding and Information Exploitation') is seeking to develop capabilities that enable military coalitions to better exploit and distribute networked information assets in the service of collective cognitive outcomes (e.g. improved decision-making). In this paper, we provide an overview of the various research activities in Project 4. We also show how these research activities complement one another in terms of supporting coalition-based collective cognition

A Distributed Merge and Split Algorithm for Fair Cooperation in Wireless Networks

This paper introduces a novel concept from coalitional game theory which allows the dynamic formation of coalitions among wireless nodes. A simple and distributed merge and split algorithm for coalition formation is constructed. This algorithm is applied to study the gains resulting from the cooperation among single antenna transmitters for virtual MIMO formation. The aim is to find an ultimate transmitters coalition structure that allows cooperating users to maximize their utilities while accounting for the cost of coalition formation. Through this novel game theoretical framework, the wireless network transmitters are able to self-organize and form a structured network composed of disjoint stable coalitions. Simulation results show that the proposed algorithm can improve the average individual user utility by 26.4% as well as cope with the mobility of the distributed users.Comment: This paper is accepted for publication at the IEEE ICC Workshop on Cooperative Communications and Networkin

Common Representation of Information Flows for Dynamic Coalitions

We propose a formal foundation for reasoning about access control policies within a Dynamic Coalition, defining an abstraction over existing access control models and providing mechanisms for translation of those models into information-flow domain. The abstracted information-flow domain model, called a Common Representation, can then be used for defining a way to control the evolution of Dynamic Coalitions with respect to information flow

Voting rules in multilateral bargaining: using an experiment to relax procedural assumptions

Experiments can be used to relax technical assumptions that are made by necessity in theoretical analysis, and further test the robustness of theoretical predictions. To illustrate this point we conduct a three-person bargaining experiment examining the effect of different decision rules (unanimity and majority rule). Our experiment implements the substantive assumptions of the Baron-Ferejohn model but imposes no structure on the timing of proposals and votes. We compare our results to those obtained from an earlier experiment which implemented the specific procedural assumptions of the model. Our results are in many ways very similar to those from the more structured experiment: we find that most games end with the formation of a minimum winning coalition, and unanimity rule is associated with greater delay. However, the earlier finding of "proposer power" is reversed. While some important patterns are robust to the less stringent implementation of procedural assumptions, our less structured experiment provides new insights into how multilateral bargaining may play out in real world environments with no strict procedural rules on timing of offers and agreements

Policy change and learning: Implementing EU environmental policies affecting agriculture

This thesis aims to show whether and how the implementation of the EU environmental policy could be improved through policy learning. The results are based on two case studies: the development of agri-environmental policy in Finland and the implementation of the Water Framework Directive(WFD)in Ireland. The institutional analysis shows that the institutional structures changed due to the membership: the formal structures changed almost overnight and, as a result of increased cross-sectoral cooperation and policy learning, the informal structures also changed. The implementation of agri-environmental policy was studied in one administrative region, namely Uusimaa, located in southern Finland. The adaptation of EU environmental policies is an interesting research topic, not only because of the policy process itself but also because of the actors and context involved

Forming Probably Stable Communities with Limited Interactions

A community needs to be partitioned into disjoint groups; each community member has an underlying preference over the groups that they would want to be a member of. We are interested in finding a stable community structure: one where no subset of members $S$ wants to deviate from the current structure. We model this setting as a hedonic game, where players are connected by an underlying interaction network, and can only consider joining groups that are connected subgraphs of the underlying graph. We analyze the relation between network structure, and one's capability to infer statistically stable (also known as PAC stable) player partitions from data. We show that when the interaction network is a forest, one can efficiently infer PAC stable coalition structures. Furthermore, when the underlying interaction graph is not a forest, efficient PAC stabilizability is no longer achievable. Thus, our results completely characterize when one can leverage the underlying graph structure in order to compute PAC stable outcomes for hedonic games. Finally, given an unknown underlying interaction network, we show that it is NP-hard to decide whether there exists a forest consistent with data samples from the network.Comment: 11 pages, full version of accepted AAAI-19 pape

Coalitional Game Theoretic Approach for Cooperative Transmission in Vehicular Networks

Cooperative transmission in vehicular networks is studied by using coalitional game and pricing in this paper. There are several vehicles and roadside units (RSUs) in the networks. Each vehicle has a desire to transmit with a certain probability, which represents its data burtiness. The RSUs can enhance the vehicles' transmissions by cooperatively relaying the vehicles' data. We consider two kinds of cooperations: cooperation among the vehicles and cooperation between the vehicle and RSU. First, vehicles cooperate to avoid interfering transmissions by scheduling the transmissions of the vehicles in each coalition. Second, a RSU can join some coalition to cooperate the transmissions of the vehicles in that coalition. Moreover, due to the mobility of the vehicles, we introduce the notion of encounter between the vehicle and RSU to indicate the availability of the relay in space. To stimulate the RSU's cooperative relaying for the vehicles, the pricing mechanism is applied. A non-transferable utility (NTU) game is developed to analyze the behaviors of the vehicles and RSUs. The stability of the formulated game is studied. Finally, we present and discuss the numerical results for the 2-vehicle and 2-RSU scenario, and the numerical results verify the theoretical analysis.Comment: accepted by IEEE ICC'1

Synergistic Team Composition

Effective teams are crucial for organisations, especially in environments that require teams to be constantly created and dismantled, such as software development, scientific experiments, crowd-sourcing, or the classroom. Key factors influencing team performance are competences and personality of team members. Hence, we present a computational model to compose proficient and congenial teams based on individuals' personalities and their competences to perform tasks of different nature. With this purpose, we extend Wilde's post-Jungian method for team composition, which solely employs individuals' personalities. The aim of this study is to create a model to partition agents into teams that are balanced in competences, personality and gender. Finally, we present some preliminary empirical results that we obtained when analysing student performance. Results show the benefits of a more informed team composition that exploits individuals' competences besides information about their personalities