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Huang M.,Carleton University | Caines P.E.,McGill University | Malhame R.P.,Ecole Polytechnique de Montreal | Malhame R.P.,Group for Research in Decision Analysis GERAD
IEEE Transactions on Automatic Control | Year: 2012

We study a class of linear-quadratic-Gaussian (LQG) control problems with N decision makers, where the basic objective is to minimize a social cost as the sum of N individual costs containing mean field coupling. The exact socially optimal solution (determining a particular Pareto optimum) requires centralized information for each agent and has high implementational complexity. As an alternative we subsequently exploit a mean field structure in the centralized optimal control problem to develop decentralized cooperative optimization so that each agent only uses its own state and a function which may be computed offline; the resulting set of strategies asymptotically achieves the social optimum as N→∞. A key feature in this scheme is to let each agent optimize a new cost as the sum of its own cost and another component capturing its social impact on all other agents. We also discuss the relationship between the decentralized cooperative solution and the so-called Nash Certainty Equivalence based solution presented in previous work on mean field LQG games. © 1963-2012 IEEE. Source

Huang M.,Carleton University | Caines P.E.,McGill University | Malhame R.P.,Ecole Polytechnique de Montreal | Malhame R.P.,Group for Research in Decision Analysis GERAD
IEEE Transactions on Automatic Control | Year: 2010

We study large population stochastic dynamic games where each agent assigns individually determined coupling strengths (with possible spatial interpretation) to the states of other agents in its performance function. The mean field methodology yields a set of decentralized controls which generates an EN-Nash equilibrium for the population of size N. A key feature of the mean field approximation (here with localized interactions) is that the resulting kth individual agent's control law uk depends on that agent's state and the precomputable weighted average trajectory of the collection of all agents each applying a decentralized control law. © 2006 IEEE. Source

Hongoh V.,University of Montreal | Michel P.,University of Montreal | Michel P.,Public Health Agency of Canada | Gosselin P.,Institute National Of Sante Publique Du Quebec Inspq | And 7 more authors.
International Journal of Environmental Research and Public Health | Year: 2016

The effects of climate change on infectious diseases are an important global health concern and necessitate decisions for allocation of resources. Economic tools have been used previously; however, how prioritization results might differ when done using broader considerations identified by local stakeholders has yet to be assessed. A multicriteria decision analysis (MCDA) approach was used to assess multi-stakeholder expressed concerns around disease prioritization via focus groups held in Quebec and Burkina Faso. Stakeholders weighted criteria and comparisons were made across study sites. A pilot disease prioritization was done to examine effects on disease rankings. A majority of identified criteria were common to both sites. The effect of context specific criteria and weights resulted in similar yet distinct prioritizations of diseases. The presence of consistent criteria between sites suggests that common concerns exist for prioritization; however, context-specific adjustments reveal much regarding resource availability, capacity and concerns that should be considered as this impacts disease ranking. Participatory decision aid approaches facilitate rich knowledge exchange and problem structuring. Furthermore, given multiple actors in low-and middle-income countries settings, multi-actor collaborations across non-governmental organizations, local government and community are important. Formal mechanisms such as MCDA provide means to foster consensus, shared awareness and collaboration. © 2016 by the authors; licensee MDPI, Basel, Switzerland. Source

Aenishaenslin C.,University of Montreal | Gern L.,University of Neuchatel | Michel P.,University of Montreal | Michel P.,Public Health Agency of Canada | And 5 more authors.
PLoS ONE | Year: 2015

Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector-borne and zoonotic diseases. © 2015 Aenishaenslin et al. Source

Ma Z.,University of Michigan | Caines P.E.,McGill University | Caines P.E.,Group for Research in Decision Analysis GERAD | Malhame R.P.,Ecole Polytechnique de Montreal
IEEE Transactions on Automatic Control | Year: 2010

Call admission and routing control decisions in stochastic loss (circuit-switched) networks with semi Markovian, multi-class, call arrival and general connection time processes are formulated as optimal stochastic control problems. The resulting so-called Hybrid Dynamic Programming equation systems take the form of vectors of partial differential equations with each component associated to a distinct distribution of routed calls over the network (i.e. distinct occupation states). This framework reduces to that of a Markov Decision Process when the traffic is Poisson and the associated computational limitations are approximately those of linear programs. Examples are provided of (i) network state space constructions and controlled state transition processes, (ii) a new closed form solution for a simple network, and (iii) the analysis and illustrative numerical results for a three link network. While the optimal control approach is in general computationally intractable, the current results permit estimates of computational requirements as well as the automatic formulation, or specification, of a range of suboptimal control problems obtained by adequately restricting admissible network routes. Furthermore the results presented constitute an essential foundation for a proposed game theoretic strategy of local optimization and global co-ordination for large loss networks (the Point Process Nash Certainty Equivalence (or Mean Field) Principle, (Ma et al. 2007-2009).) © 2010 IEEE. Source

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