Professor Jerzy Filar

Professor and Director of CARM

Mathematics
Faculty of Science
j.filar@uq.edu.au
+61 7 336 52236

Overview

Jerzy Filar is the Director of the Centre for Applications in Natural Resource Mathematics within the School of Mathematics and Physics. Jerzy is a broadly trained applied mathematician with research interests spanning a spectrum of both theoretical and applied topics in Operations Research, Stochastic Modelling, Optimisation, Game Theory and Environmental Modelling. Professor Filar co-authored, or authored, five books or monographs and approximately 100 refereed research papers. He has a record of research grants/contracts with agencies and research institutes such as NSF, ARC, US EPA, World Resources Institute, DSTO and the Sir Keith and Sir Ross Smith Foundation. He is editor-in-chief of Environmental Modelling and Assessment and serves on editorial boards of Journal of Mathematical Analysis and Applications and a number of other journals. He has supervised or co-supervised 23 PhD students. Jerzy's Erdos Number is 3.

Research Interests

  • Stochastic Modelling
    Markov Decision Processes, Stochastic Games, Risk.
  • Analytic Perturbation Theory and Applications
    Regular and singular perturbations of matrices and operators and their applications to optimisation and Markov chains.
  • Operations Research and Optimisation
    Linear, nonlinear and dynamic programming. Applications to patient flow modelling, airport recovery problem, electricity grid operations.
  • Environmental Modelling
    Sustainable fisheries, sustainability and the times scales conjecture, cascading errors in complex models of the environment, evolutionary games.
  • Graph Theory
    Hamiltonian cycle problem, spectral properties of regular graphs.
  • Game Theory
    Non-cooperative dynamic games, games with incompetent players, applications of game theory.

Research Impacts

In his role as CARM Director, Professor Filar and the team are partnering with Queensland’s Department of Agriculture and Fisheries (DAF) to equip their stock assessments with the very latest statistical and mathematical modelling methodologies to support the Sustainable Fisheries Strategy. As fisheries are not fully observable and fish numbers vary as they are lost to predators , disease, aging, fishing pressures and other environmental factors it is very challenging to devise reliable assessments and sustainable harvest levels that deliver economic benefits without dangerously depleting fish stocks. This is where mathematical and statistical modelling as well as computer simulations offer an effective and risk-free approach to estimate likely impacts of any proposed change.

Qualifications

  • Bachelor of Science, University of Melbourne
  • Master of Science, Monash University
  • Master of Arts, University of Illinois
  • Doctor of Philosophy, University of Illinois

Publications

  • Filar, Jerzy A., Qiao, Zhihao and Streipert, Sabrina (2020). Risk sensitivity in Beverton-Holt fishery with multiplicative harvest. Natural Resource Modeling e12257 doi: 10.1111/nrm.12257

  • Filar, Jerzy A., Qiao, Zhihao and Ye, Nan (2019). POMDPs for sustainable fishery management. International Congress on Modelling and Simulation, Canberra, Australia, 1-6 December 2019. Modelling and Simulation Society of Australia and New Zealand. doi: 10.36334/modsim.2019.g2.filar

  • Filar, Jerzy A. (2018). Foreword. Environmental Modeling and Assessment, 23 (6), 609-610. doi: 10.1007/s10666-018-9645-z

View all Publications

Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Available Projects

  • Mathematical models of environmental problems often demand understanding of complex dynamics and interactions between many physical and biological variables on the one hand, and human inputs on the other. Uncertainties accompanying such models stem from multiple sources. Sometimes they manifest themselves as cascading errors and at other times they involve the risk of key variables crossing undesirable thresholds. In both cases they undermine confidence in either the model or, worse still, the underlying science.

    The accompanying mathematical problems can be studied using a wide range of approaches including (but not limited to) perturbation theory, stochastic processes, partially observable Markov decision processes, statistical methods, dynamical systems and simulation. They can also be applied in several important contexts including (but not limited to) conservation of natural resources, optimizing harvests of fish subject to sustainability constraints or generating warning signals for species whose abundance drops to low levels. One particularly challenging problem is that of designing controls that minimize the probability of a catastrophe, consistently over time, while achieving satisfactory and sustainable resource consumption. A related problem, also stemming from fishery science applications, is that of devising a “balanced harvest” strategy that ultimately restores the proportions of age cohorts of the harvested species to those that are natural for that species.

    There are several PhD, Masters’ or Honours’ research projects that can be designed on this general theme and tailored to the particular student’s background and interests. For some projects co-supervision with scientists from the Queensland Department of Agriculture and Fisheries, or CSIRO may be required.

  • Review and evaluate efficient sampling programs: Is the right amount of sampling occurring for each species? Are there any significant biases in the sampling programs for each species? Assess whether routine analyses are being carried out correctly and to develop new analyses for fisheries management.

    Project components include developing: Quantitative analyses to optimise fishery-dependent sampling across multiple species and regions. Routine methods for assessing precision of current sampling of fish length and age. New methods for turning fish length and age data into advice (indicators) about fishing pressure and the status of fish stocks. A corresponding harvest strategy and reference points for judging the performance of the indicators.

  • Improved estimation of state-wide recreational harvests, including resampling, bootstrap and MCMC techniques. Quantify changes in survey angler avidity and recall bias between survey years and methodologies; adjust previous survey data to obtain improved estimates. Evaluating sampling frames - develop methods to generate state-wide harvest estimates (and associated measures of uncertainty) from several synchronous samples taken from different sampling frames (e.g. a licence frame and a residential telephone number list). Develop hierarchical and conditional mixed models for estimation of recreational fish catch and catch rates. Investigate the statistical modelling of recreational survey data collected from multiple survey methods.

    From survey to analysis: dealing with differences in the scale at which survey data are collected and the scale at which data are analysed. Examine appropriate estimation methods for different fish species. Develop statistical methods for low fish abundance or recreational species caught by ‘hard-to-reach’ fishers. Develop methods to engage and retain recreational fishers in volunteer data contribution programs.

View all Available Projects

Publications

Book

Book Chapter

  • Haythorpe, Michael and Filar, Jerzy A. (2014). A linearly-growing conversion from the set splitting problem to the directed Hamiltonian cycle problem. Optimization and control methods in industrial engineering and construction. (pp. 35-52) edited by Honglei Xu and Xiangyu Wang. Dordrecht, The Netherlands: Kluwer Academic Publishers. doi: 10.1007/978-94-017-8044-5_3

  • Boland, J., Pudney, P. and Filar, Jerzy A. (2013). Electricity Supply Without Fossil Fuels. Computational Intelligent Data Analysis for Sustainable Development. (pp. 489-497) edited by Ting Yu, Nitesh V. Chawla and Simeon Simoff. Boca Raton Florida, United States: Chapman and Hall / CRC Press.

  • Chiera, Belinda A., Filar, Jerzy A., Zachary, Daniel S. and Gordon, Adrian H. (2009). Comparative forecasting and a test for persistence in the El Nino Southern Oscillation. Uncertainty in environmental decision making: a handbook of research and best practice. (pp. 253-272) edited by Jerzy A. Filar and Alain Haurie. New York, United States: Springer. doi: 10.1007/978-1-4419-1129-2_9

  • Filar, Jerzy A., Hudson, Irene, Matthew, Thomas and Sinha, Bimal (2008). Analytic perturbations and systematic bias in statistical modeling and inference. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen. (pp. 17-34) edited by N. Balakrishnan, Edsel A. Peña and Mervyn J. Silvapulle. Beachwood, Ohio, United States: Institute of Mathematical Statistics. doi: 10.1214/193940307000000022

  • Beck, Justin and Filar, Jerzy A. (2007). Games incompetence and training. Advances in dynamic game theory: numerical methods, algorithms, and applications to ecology and economics. (pp. 93-110) edited by Steffen Jørgensen, Marc Quincampoix and Thomas L. Vincent. Boston, MA, United States: Birkhauser. doi: 10.1007/978-0-8176-4553-3_5

  • Filar J.A. and Kang B. (2006). Two types of risk. (pp. 109-140) Springer New York LLC.

  • Filar, Jerzy A., Manyem, Prabhu, Visser, Marc Simon and White, Kevin (2003). Air traffic management at Sydney with cancellations and curfew penalties. Optimization and industry: new frontiers. (pp. 113-140) edited by Panos M. Pardalos and Victor Korotkikh. Boston, USA: Kluwer. doi: 10.1007/978-1-4613-0233-9_5

  • Lin, Yuanlie, Filar, Jerzy A. and Liu, Ke (2002). Finite horizon portfolio risk models with probability criterion. Markov processes and controlled Markov chains. (pp. 405-424) Dordrecht, The Netherlands: Kluwer Academic Publishers. doi: 10.1007/978-1-4613-0265-0_26

  • Filar, Jerzy A. and Xianping, Guo (2002). Linear program for communicating MDPs with multiple constraints. Markov processes and controlled Markov chains. (pp. 245-254) edited by Zhenting Hou, Jerzy A. Filar and Anyue Chen. Dordecht, Netherlands: Kluwer. doi: 10.1007/978-1-4613-0265-0_14

  • Filar, J. A. (2002). Mathematical Models. Knowledge for Sustainable Development: An Insight into the Encyclopedia of Life Support Systems. (pp. 339-354) Johannesburg, South Africa: UNESCO/EOLSS.

  • Avrachenkov, Konstantin E., Filar, Jerzy and Haviv, Moshe (2002). Singular perturbations of Markov chains and decision processes. Handbook of Markov decision processes: methods and applications. (pp. 113-150) edited by Eugene A. Feinberg and Adam Shwartz. Boston, United States: Kluwer Academic Publishers. doi: 10.1007/978-1-4615-0805-2_4

  • Andramonov, Mikhail, Filar, Jerzy A., Pardalos, Pardalos and Rubinov, Alexander (2000). Hamiltonian cycle problem via Markov chains and min-type applications. Approximation and complexity in numerical optimization: continuous and discrete problems. (pp. 31-47) edited by Panos M. Pardalos. Dordrecht, Netherlands: Springer US. doi: 10.1007/978-1-4757-3145-3_3

  • Connell, S. A., Filar, Jerzy A., Szczechla, W. W. and Vrieze, O. J. (1999). Discounted stochastic games, a complex analytic perspective. Stochastic and differential games: theory and numerical methods. (pp. 271-296) edited by Bardi Martino, T. E. S. Raghavan and T. Parthasarathy. Boston, UK: Birkhauser. doi: 10.1007/978-1-4612-1592-9_6

  • Filar, J. A. and Haurie, A. (1998). Uncertainty in Environmental Models: Dynamic Systems Perspective. The Co-Action between Living Systems and the Planet. (pp. 283-302) edited by Greppin, H., Degli Agosti, R. and Pennel, C.. Geneva, Switzerland: University of Geneva Press.

  • Filar, J. A. and Liu, Ke (1997). Hamiltonian Cycle Problem and a Singularly Perturbed Markov Decision Process. Statistics, probability, and game theory : papers in honor of David Blackwell. (pp. 45-63) edited by Ferguson, T., Shapley, L. S. and MacQueen, J. B.. USA: Institute of Mathematical Statistics.

  • Curiel, I., Filar, J. A. and Zapert, R. (1997). Relative Contribution of the Enhanced Greenhouse Effect on the Coastal Changes in Louisiana. Modeling Environmental Policy. (pp. 161-184) edited by Martin, W. E. and McDonald, L. A.. New York, USA: Kluwer.

  • Filar, J. A. and Zapert, R. (1996). Uncertainty Analysis of a Greenhouse Model. Operations Research and Environmental Management. (pp. 101-118) edited by Haurie, A. and Carraro, C.. Dordrecht, The Netherlands: Kluwer.

  • Chen, Ming and Filar, J. A. (1992). Hamiltonian Cycles, Quadratic Programming, and Ranking of Extreme Points. Recent Advances in Global Optimization. (pp. 32-49) edited by Floudas, C. and Pardalos, P.. USA: Princeton University Press.

  • Abbad, M. and Filar, J. A. (1992). Singularly Perturbed Limiting Average Stochastic Game Problems. Game theory and economic applications. (pp. 69-97) Germany: Spring.

  • Braddock, R. D., Filar, J. A. and Zapert, R. (1992). System and Control Theory Perspectives of the IMAGE Greenhouse Model. Stochastic Theory and Adaptive Control. (pp. 54-68) edited by Duncan, T. and Pasik-Duncan, B.. Heidelberg, Germany: Springer-Verlag.

  • Filar, Jerzy A. and Tolwinski, Boleslaw (1991). On the algorithm of Pollatschek and Avi-Itzhak. Stochastic games and related topics. (pp. 59-70) edited by T. E. S. Raghavan, T. S. Ferguson, T. Parthasarathy and O. J. Vrieze. Amsterdam, The Netherlands: Springer. doi: 10.1007/978-94-011-3760-7_6

  • Braddock, R. D. and Filar, J. A. (1991). Response times of the ocean. Coastal engineering: climate for change. (pp. 22-27) edited by Robert G. Bell. Hamilton, New Zealand: DSIR Marine and Fishwater.

Journal Article

Conference Publication

  • Filar, Jerzy A., Qiao, Zhihao and Ye, Nan (2019). POMDPs for sustainable fishery management. International Congress on Modelling and Simulation, Canberra, Australia, 1-6 December 2019. Modelling and Simulation Society of Australia and New Zealand. doi: 10.36334/modsim.2019.g2.filar

  • Diao, Jiahao, Nazarathy, Yoni , Taimre, Thomas and Filar, Jerzy A. (2017). To fish or cut bait?. 2017 11th Asian Control Conference (ASCC), Gold Coast, QLD, Australia, 17 - 20 December 2017. Piscataway, NJ, United States: IEEE. doi: 10.1109/ASCC.2017.8287563

  • Ben-Tovim, D. I., Filar, J. A., Hakendorf, P. H., Qin, S., Thompson, C. H. and Ward, D. A. (2015). Hospital Event Simulation Model: Arrivals to Discharge. MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Broadbeach, Queensland, Australia, 29 November - 4 December 2015. Canberra, ACT Australia: Modelling and Simulation Society of Australia and New Zealand.

  • Clissold, A., Filar, J., Qin, S. and Ward, D. (2015). Markov decision process model for optimisation of patient flow. MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Broadbeach, QLD, Australia, 29 Nov - 4 Dec 2015. Broadbeach, QLD, Australia: The Modelling and Simulation Society of Australia and New Zealand.

  • Ejov, V, Filar, J and Gondzio, J (2004). An Interior point heuristic for the hamiltonian cycle problem via markov decision processes. 4th International Conference on Frontiers in Global Optimization, Santorini Greece, Jun 08-12, 2003. doi: 10.1023/B:JOGO.0000044772.11089.1a

  • Filar, J. A., Gondzio, J., Haurie, A., Moresino, F. and Vial, J. -P. (2000). Decomposition and parallel processing techniques for two-time scale controlled Markov chains. 39th IEEE Conference on Decision and Control, Sydney, Australia, 12 - 15 December 2000. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/CDC.2000.912851

  • Filar, J. A. and Haurie, A. (1997). Optimal Ergodic Control of Singularly Perturbed Hybrid Stochastic Systems. 1996 AMS-SIAM Summer Seminar, Williamsburg, Virginia, USA, 17-22 June, 1996. Providence, RI United States: American Mathematical Society.

  • Filar, J. A., Gaertner, P. S. and Janssen, M. A. (1996). An application of optimization to the problem of climate change. Conference on the State of the Art in Global Optimization - Computational Methods and Applications, Princeton Nj, Apr 28-30, 1995. Kluwer.

  • Bielecki, T. R., Filar, J. A. and Gaitsgory, V. (1996). Asymptotic Analysis of a Stochastic Manufacturing System with Slow and Fast Motions. 35th IEEE Conference on Decision and Control, Japan, 13 December 1996. Piscataway NJ United States: IEEE.

  • Abbad Mohammed and Filar Jerzy A. (1992). Perturbation theory for semi-Markov control problems. Publ by IEEE.

  • Abbad, M. and Filar, J. A. (1991). Aggregation-disaggregation algorithm for epsilon /sup 2/-singularly perturbed limiting average Markov control problems. 30th IEEE Conference on Decision and Control 1991, Brighton, UK, 11-13 December 1991. Piscataway NJ United States: IEEE.

  • Filar, Jerzy A., Krass, Dmitry and Ross, Keith (1989). Percentile objective criteria in limiting average Markov control problems. 28th IEEE Conference on Decision and Control, Tampa FL, USA, 13-15 Dec 1989. IEEE. doi: 10.1109/CDC.1989.70342

  • Filar, Jerzy A. and Krass, Dmitry (1987). The Embedding of the Traveling Salesman Problem in a Markov Decision Process. 26th IEEE Conference on Decision and Control, Los Angeles, California, USA, 9-11 December 1987. Piscataway NJ United States: IEEE Control Systems Society. doi: 10.1109/CDC.1987.272943

  • Filar, J. A. and Lee, H. M. (1985). Gain/variability tradeoffs in undiscounted Markov decision processes. 24th IEEE Conference on Decision and Control, Fort Lauderdale, FL, United States, 11-13 December 1985. Piscataway, NJ, United States: Institute of Electrical and Electronic Engineers. doi: 10.1109/CDC.1985.268672

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Mathematical models of environmental problems often demand understanding of complex dynamics and interactions between many physical and biological variables on the one hand, and human inputs on the other. Uncertainties accompanying such models stem from multiple sources. Sometimes they manifest themselves as cascading errors and at other times they involve the risk of key variables crossing undesirable thresholds. In both cases they undermine confidence in either the model or, worse still, the underlying science.

    The accompanying mathematical problems can be studied using a wide range of approaches including (but not limited to) perturbation theory, stochastic processes, partially observable Markov decision processes, statistical methods, dynamical systems and simulation. They can also be applied in several important contexts including (but not limited to) conservation of natural resources, optimizing harvests of fish subject to sustainability constraints or generating warning signals for species whose abundance drops to low levels. One particularly challenging problem is that of designing controls that minimize the probability of a catastrophe, consistently over time, while achieving satisfactory and sustainable resource consumption. A related problem, also stemming from fishery science applications, is that of devising a “balanced harvest” strategy that ultimately restores the proportions of age cohorts of the harvested species to those that are natural for that species.

    There are several PhD, Masters’ or Honours’ research projects that can be designed on this general theme and tailored to the particular student’s background and interests. For some projects co-supervision with scientists from the Queensland Department of Agriculture and Fisheries, or CSIRO may be required.

  • Review and evaluate efficient sampling programs: Is the right amount of sampling occurring for each species? Are there any significant biases in the sampling programs for each species? Assess whether routine analyses are being carried out correctly and to develop new analyses for fisheries management.

    Project components include developing: Quantitative analyses to optimise fishery-dependent sampling across multiple species and regions. Routine methods for assessing precision of current sampling of fish length and age. New methods for turning fish length and age data into advice (indicators) about fishing pressure and the status of fish stocks. A corresponding harvest strategy and reference points for judging the performance of the indicators.

  • Improved estimation of state-wide recreational harvests, including resampling, bootstrap and MCMC techniques. Quantify changes in survey angler avidity and recall bias between survey years and methodologies; adjust previous survey data to obtain improved estimates. Evaluating sampling frames - develop methods to generate state-wide harvest estimates (and associated measures of uncertainty) from several synchronous samples taken from different sampling frames (e.g. a licence frame and a residential telephone number list). Develop hierarchical and conditional mixed models for estimation of recreational fish catch and catch rates. Investigate the statistical modelling of recreational survey data collected from multiple survey methods.

    From survey to analysis: dealing with differences in the scale at which survey data are collected and the scale at which data are analysed. Examine appropriate estimation methods for different fish species. Develop statistical methods for low fish abundance or recreational species caught by ‘hard-to-reach’ fishers. Develop methods to engage and retain recreational fishers in volunteer data contribution programs.