The current project is entitled “Multidimensional ambiguous stochastic dynamical models with applications to Cyprus population distribution (MultiPOP)” (POST-DOC/0916/0139). The project is implemented within the “Framework Programme for Research, Technological Development and Innovation RESTART 2016-2010” (Pillar II: Sustainable RTDI system – DIDAKTOR Programme) of the Cyprus Research Promotion Foundation (RPF). The project is co-funded by the European Regional Development Fund and the Republic of Cyprus, through the RPF. The project duration is 36 months (to be completed by October 31st, 2021).
The proposed research project involves both interesting and challenging theoretical and application-oriented research in the context of complex and ambiguous stochastic multidimensional population models for policy-making and planning. The main objective is to provide a new approach to the modeling and analysis of stochastic dynamical population models subject to uncertainty and ambiguity to the stage that these tools are readily accessible not only to the research world but also to demographers, social scientists, and government agencies, and can be applied generically across the field of population sciences.
The consortium of the present project is comprised of a balanced team of research organizations and an individual researcher. In particular, it involves three universities namely the University of Cyprus (Cyprus), the University of Ottawa (Canada), and Aalto University (Finland), and an individual researcher from the Italian National Institute of Statistics.
In this project, we will develop a new Science, Technology, Engineering, and Mathematics (STEM) approach to the modeling and analysis of dynamical population models through the study of modern systems and stochastic control theory. Our aim is to provide a general mathematical framework for the study of multidimensional ambiguous stochastic dynamical models with application to the Cyprus population for policy-making and planning. In particular, through this project we wish: (i) first to develop a mathematical tool known as the Total Variation distance metric that captures the “ambiguity effect” on stochastic dynamical models, and codifies the impact of incorrect dynamical models on the performance of optimal decisions; (ii) then apply this tool to model the short-term and long-term distribution of the population by considering different categorical variables/dimensions, using the interesting case study of Cyprus; (iii) develop efficient parameter estimation algorithms that center on the estimation of missing statistical data necessary for the analysis of multidimensional population models. We believe that the developed multidimensional ambiguous stochastic dynamical population models and the identified parameters will be useful aids in governmental efforts to address some of the fundamental questions that concern Cyprus society, such as: (a) what is the role of immigration in Cyprus society, and how should the target level of immigration be determined so as to satisfy the work power demand and keep the unemployment rate low, and (b) what economic policies should the Cyprus government adopt to support the needs of different population groups and at the same time safeguard robust public finances, necessary to maintain internal macroeconomic stability. The long-term objective of the study is to gain an increased understanding of the natural evolution of different population groups and how they interact with each other which may, in turn, lead to a better understanding of social and political problems and lead to improved, more efficient, and viable socio-economic policies.
In this project, we will develop a new STEM approach to the modeling and analysis of ambiguous stochastic dynamical models for addressing the risk of having incorrect population models and to deal with the optimality of stochastic control policies. The developed approach is proposed as an improved tool, which can be used by scientists, demographers, and government agencies so that better and more efficient political and economic strategies can be formulated in the future. For the implementation of the project, a well-organized consortium has been created consisting of leading researchers in stochastic control systems, system identification, statistics and demography, and social and political sciences. The formation of this collaborative team presents the opportunity for considerable advancement in population dynamics for policy-making and planning, through the study of modern systems and stochastic control theory.
The project structure has been organized into seven work packages numbered from WP1 to WP7, representing: one management work package, WP1; one dissemination work package, WP2; and five technical research & development work packages, WP3 through WP7. WP1 and WP2 span the entire duration of the project and are intended to monitor the progress of the project and the dissemination of the results.
|HO: University of Cyprus||Cyprus||www.ucy.ac.cy|
|FRO1: University of Ottawa
|FRO2: Aalto University
|D1||Interim progress report||WP1||Completed|
|D2||Final progress report||WP1||Pending|
|D3||Minutes/Agenda of the consortium meetings||WP1||Pending|
|D4||Workshop package (agenda/invitation /pdf presentation/list of participants)||WP2||Pending|
|D5||List of publications and other relevant activities||WP2||Pending|
|D6||Published research papers||WP2||Pending|
|D8||Modeling methods for ambiguous stochastic control systems (Report)||WP3||Completed|
|D9||Optimality criteria including the principle of optimality and dynamic programming (Report)||WP3||Completed|
|D10||Multidimensional stochastic dynamical population models (Report)||WP4||Completed|
|D11||Simulation package (Software)||WP4||Completed|
|D12||Mathematical methods and algorithms for estimation and identification of system parameters (Report)||WP5||Pending|
|D13||Simulation package (Software)||WP5||Pending|
|D14||Optimum immigration and job creation policies (Report)||WP6||Pending|
|D15||Simulation package (Software)||WP6||Pending|
|D16||Costing of several socio-economic programs (Report)||WP7||Pending|
|D17||Simulation package (Software)||WP7||Pending|
Dissemination of Results
| Infinite Horizon Average Cost Dynamic Programming subject to Total Variation Distance Ambiguity||Ioannis Tzortzis, Charalambos D. Charalambous and Themistoklis Charalambous*||University of Cyprus, Aalto University*||SIAM Journal on Control and Optimization (SICON)|
| Robust LQG for Markov Jump Linear Systems||Ioannis Tzortzis, Charalambos D. Charalambous, and Christoforos N. Hadjicostis||University of Cyprus||IEEE Conference on Decision and Control (CDC 2019)|
| Canonical Dynamic Programming Equations subject to Ambiguity||Ioannis Tzortzis and Charalambos D. Charalambous||University of Cyprus||IFAC World Congress (IFAC 2020)|
| Jump LQR Systems with Unknown Transition Probabilities||Ioannis Tzortzis, Charalambos D. Charalambous, and Christoforos N. Hadjicostis||University of Cyprus||IEEE Transactions on Automatic Control (TACON)|
|Modelling Population Dynamics App||MPD App (version 1)|