Jerzy Filar is Emeritus Professor of Applied Mathematics. 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, FRDC and the Sir Keith and Sir Ross Smith Foundation. He is editor-in-chief of Springer’s Environmental Modelling and Assessment and served on editorial boards of several other journals. He has supervised or co-supervised 29 PhD students. Jerzy's Erdos Number is 3.
In his last role as CARM Director, Professor Filar and the team are partnered 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.
Journal Article: Capturing episodic impacts of environmental signals
Mendiolar, M., Filar, J.A., Yang, W.-H., Leahy, S. and Courtney, A.J. (2023). Capturing episodic impacts of environmental signals. Environmental Modelling and Software, 170 105837, 1-19. doi: 10.1016/j.envsoft.2023.105837
Journal Article: Multi-pass Bayesian estimation: a robust Bayesian method
Lei, Yeming, Zhou, Shijie, Filar, Jerzy and Ye, Nan (2023). Multi-pass Bayesian estimation: a robust Bayesian method. Computational Statistics, 39 (4), 2183-2216. doi: 10.1007/s00180-023-01390-0
Conference Publication: Estimating recreational catch
Mendiolar, Manuela, Filar, Jerzy A., O'Neill, Michael F., Martin, Tyson, Teixeira, Daniella, Webley, James and Holden, Matthew (2023). Estimating recreational catch. 25th International Congress on Modelling and Simulation, Darwin, NT Australia, 9 to 14 July 2023. Canberra, ACT Australia: Modelling and Simulation Society of Australia and New Zealand. doi: 10.36334/modsim.2023.mendiolar
Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries
(2021–2024) ARC Discovery Projects
Modelling environmental changes and effects on wild-caught species in Queensland
(2019–2021) Fisheries Research & Development Corporation
Time Consistency, Risk-Mitigation and Partially Observable Systems
(2018–2023) ARC Discovery Projects
Parametric sensitivity of threshold risk and multi-absorption phase type distributions
(2024) Doctor Philosophy
Evolutionary games under incompetence & foraging strategies of marine bacteria
(2019) Doctor Philosophy
On quantitative indices and modelling of harvested fish populations
(2024) Doctor Philosophy
Risk and Uncertainty Quantification in Environmental Modelling
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.
Fishery-dependent monitoring of Queensland's fisheries
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.
Queensland state-wide estimation of recreational fish catches
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.
Genetic theory for cubic graphs
Baniasadi, Pouya, Ejov, Vladimir, Filar, Jerzy A. and Haythorpe, Michael (2016). Genetic theory for cubic graphs. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-19680-0
Analytic perturbation theory and its applications
Avrachenkov, K., Filar, J. A. and Howlett, P. G. (2013). Analytic perturbation theory and its applications. Philadelphia, United States: Society for Industrial and Applied Mathematics (SIAM). doi: 10.1137/1.9781611973143
Hamiltonian cycle problem and markov chains
Borkar, V. S., Ejov, V., Filar, J. A. and Nguyen, G. T. (2012). Hamiltonian cycle problem and markov chains. New York, NY, United States: Springer.
Uncertainty in Environmental Decision Making
Filar, J. A. and Haurie, A. eds. (2009). Uncertainty in Environmental Decision Making. New York, USA: Springer-Verlag.
Markov Processes and Controlled Markov Chains
Hou, Z., Filar, J. A. and Chen, A. eds. (2002). Markov Processes and Controlled Markov Chains. Boston, USA: Kluwer.
Competitive Markov Decision Processes - Theory, Algorithms, and Applications
Filar, Jerzy and Vrieze, Koos (1997). Competitive Markov Decision Processes - Theory, Algorithms, and Applications. New York, United States: Springer.
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
Electricity Supply Without Fossil Fuels
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.
Comparative forecasting and a test for persistence in the El Nino Southern Oscillation
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
Analytic perturbations and systematic bias in statistical modeling and inference
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
Games incompetence and training
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.
Air traffic management at Sydney with cancellations and curfew penalties
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
Finite horizon portfolio risk models with probability criterion
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
Linear program for communicating MDPs with multiple constraints
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.
Singular perturbations of Markov chains and decision processes
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
Hamiltonian cycle problem via Markov chains and min-type applications
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
Discounted stochastic games, a complex analytic perspective
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
Uncertainty in Environmental Models: Dynamic Systems Perspective
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.
Hamiltonian Cycle Problem and a Singularly Perturbed Markov Decision Process
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.
Relative Contribution of the Enhanced Greenhouse Effect on the Coastal Changes in Louisiana
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.
Uncertainty Analysis of a Greenhouse Model
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.
Hamiltonian Cycles, Quadratic Programming, and Ranking of Extreme Points
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.
Singularly Perturbed Limiting Average Stochastic Game Problems
Abbad, M. and Filar, J. A. (1992). Singularly Perturbed Limiting Average Stochastic Game Problems. Game theory and economic applications. (pp. 69-97) Germany: Spring.
System and Control Theory Perspectives of the IMAGE Greenhouse Model
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.
On the algorithm of Pollatschek and Avi-Itzhak
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.
Capturing episodic impacts of environmental signals
Mendiolar, M., Filar, J.A., Yang, W.-H., Leahy, S. and Courtney, A.J. (2023). Capturing episodic impacts of environmental signals. Environmental Modelling and Software, 170 105837, 1-19. doi: 10.1016/j.envsoft.2023.105837
Multi-pass Bayesian estimation: a robust Bayesian method
Lei, Yeming, Zhou, Shijie, Filar, Jerzy and Ye, Nan (2023). Multi-pass Bayesian estimation: a robust Bayesian method. Computational Statistics, 39 (4), 2183-2216. doi: 10.1007/s00180-023-01390-0
Where Do Mistakes Lead? A Survey of Games with Incompetent Players
Graham, Thomas, Kleshnina, Maria and Filar, Jerzy A. (2023). Where Do Mistakes Lead? A Survey of Games with Incompetent Players. Dynamic Games and Applications, 13 (1), 231-264. doi: 10.1007/s13235-022-00425-3
Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations
McKerral, Jody C., Kleshnina, Maria, Ejov, Vladimir, Bartle, Louise, Mitchell, James G. and Filar, Jerzy A. (2023). Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations. PLoS One, 18 (2) e0279838, 1-17. doi: 10.1371/journal.pone.0279838
Hidden equations of risk critical thresholds
Ejov, Vladimir V., Filar, Jerzy A. and Qiao, Zhihao (2023). Hidden equations of risk critical thresholds. Stochastic Models, 39 (2), 383-413. doi: 10.1080/15326349.2022.2108452
Shifts in evolutionary balance of phenotypes under environmental changes
Kleshnina, Maria, McKerral, Jody C., González-Tokman, Cecilia, Filar, Jerzy A. and Mitchell, James G. (2022). Shifts in evolutionary balance of phenotypes under environmental changes. Royal Society Open Science, 9 (11) 220744, 1-17. doi: 10.1098/rsos.220744
Square root identities for harvested Beverton–Holt models
Filar, Jerzy and Streipert, Sabrina (2022). Square root identities for harvested Beverton–Holt models. Journal of Theoretical Biology, 549 111199, 1-14. doi: 10.1016/j.jtbi.2022.111199
Mistakes can stabilise the dynamics of rock-paper-scissors games
Kleshnina, Maria, Streipert, Sabrina S., Filar, Jerzy A. and Chatterjee, Krishnendu (2021). Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational Biology, 17 (4) e1008523, e1008523. doi: 10.1371/journal.pcbi.1008523
Prioritised learning in snowdrift-type games
Kleshnina, Maria, Streipert, Sabrina S., Filar, Jerzy A. and Chatterjee, Krishnendu (2020). Prioritised learning in snowdrift-type games. Mathematics, 8 (11) 1945, 1-20. doi: 10.3390/math8111945
Risk sensitivity in Beverton-Holt fishery with multiplicative harvest
Filar, Jerzy A., Qiao, Zhihao and Streipert, Sabrina (2020). Risk sensitivity in Beverton-Holt fishery with multiplicative harvest. Natural Resource Modeling, 33 (3) e12257. doi: 10.1111/nrm.12257
Hamiltonian Cycles and Subsets of Discounted Occupational Measures
Eshragh, Ali, Filar, Jerzy A., Kalinowski, Thomas and Mohammadian, Sogol (2020). Hamiltonian Cycles and Subsets of Discounted Occupational Measures. Mathematics of Operations Research, 45 (2), 713-731. doi: 10.1287/moor.2019.1009
Ben-Tovim, David, Bogomolov, Tim, Filar, Jerzy, Hakendorf, Paul, Qin, Shaowen and Thompson, Campbell (2020). Hospital’s instability wedges. Health Systems, 9 (3), 202-211. doi: 10.1080/20476965.2018.1524407
Filar, Jerzy A. (2018). Foreword. Environmental Modeling and Assessment, 23 (6), 609-610. doi: 10.1007/s10666-018-9645-z
Linearly-growing reductions of Karp's 21 NP-complete problems
Filar, Jerzy, Haythorpe, Michael and Taylor, Richard (2018). Linearly-growing reductions of Karp's 21 NP-complete problems. Numerical Algebra, Control and Optimization, 8 (1), 1-16. doi: 10.3934/naco.2018001
Evolutionary games under incompetence
Kleshnina, Maria, Filar, Jerzy A., Ejov, Vladimir and McKerral, Jody C. (2018). Evolutionary games under incompetence. Journal of Mathematical Biology, 77 (3), 627-646. doi: 10.1007/s00285-018-1221-2
A note on using the resistance-distance matrix to solve Hamiltonian cycle problem
Ejov, V., Filar, J. A., Haythorpe, M., Roddick, J. F. and Rossomakhine, S. (2017). A note on using the resistance-distance matrix to solve Hamiltonian cycle problem. Annals of Operations Research, 261 (1-2), 393-399. doi: 10.1007/s10479-017-2571-7
Hospital Event Simulation Model: Arrivals to Discharge - design, development and application
Ben-Tovim, D., Filar, J., Hakendorf, P., Qin, S., Thompson, C. and Ward, D. (2016). Hospital Event Simulation Model: Arrivals to Discharge - design, development and application. Simulation Modelling Practice and Theory, 68, 80-94. doi: 10.1016/j.simpat.2016.07.004
On transition matrices of Markov chains corresponding to Hamiltonian cycles
Avrachenkov, Konstantin, Eshragh, Ali and Filar, Jerzy A. (2016). On transition matrices of Markov chains corresponding to Hamiltonian cycles. Annals of Operations Research, 243 (1-2), 19-35. doi: 10.1007/s10479-014-1642-2
Australian electricity market and price volatility
Boland, J., Filar, J. A., Mohammadian, G. and Nazari, A. (2016). Australian electricity market and price volatility. Annals of Operations Research, 241 (1-2), 357-372. doi: 10.1007/s10479-011-1033-x
Singularly perturbed linear programs and Markov decision processes
Avrachenkov, Konstantin, Filar, Jerzy A., Gaitsgory, Vladimir and Stillman, Andrew (2016). Singularly perturbed linear programs and Markov decision processes. Operations Research Letters, 44 (3), 297-301. doi: 10.1016/j.orl.2016.02.005
A new heuristic for detecting non-Hamiltonicity in cubic graphs
Filar, Jerzy A., Haythorpe, Michael and Rossomakhine, Serguei (2015). A new heuristic for detecting non-Hamiltonicity in cubic graphs. Computers and Operations Research, 64, 283-292. doi: 10.1016/j.cor.2015.06.004
Hamiltonian cycle curves in the space of discounted occupational measures
Filar, Jerzy A. and Moeini, Asghar (2015). Hamiltonian cycle curves in the space of discounted occupational measures. Annals of Operations Research, 317 (2), 605-622. doi: 10.1007/s10479-015-2030-2
Sustainability screw: role of relative production and abatement time scales
Filar, Jerzy A., Krawczyk, Jacek B. and Agrawal, Manju R. (2015). Sustainability screw: role of relative production and abatement time scales. Journal of the Operational Research Society, 66 (8), 1259-1269. doi: 10.1057/jors.2014.39
Deterministic "Snakes and Ladders" heuristic for the Hamiltonian cycle problem
Baniasadi, Pouya, Ejov, Vladimir, Filar, Jerzy A., Haythorpe, Michael and Rossomakhine, Serguei (2014). Deterministic "Snakes and Ladders" heuristic for the Hamiltonian cycle problem. Mathematical Programming Computation, 6 (1), 55-75. doi: 10.1007/s12532-013-0059-2
Filar, J. A., Haythorpe, M. A. and Murray, W. (2013). On the determinant and its derivatives of the rank-one corrected generator of a Markov chain on a graph. Journal of Global Optimization, 56 (4), 1425-1440. doi: 10.1007/s10898-012-9855-x
Markov chains, Hamiltonian cycles and volumes of convex bodies
Borkar, Vivek S. and Filar, Jerzy A. (2013). Markov chains, Hamiltonian cycles and volumes of convex bodies. Journal of Global Optimization, 55 (3), 633-639. doi: 10.1007/s10898-011-9819-6
Incompetence and impact of training in Bimatrix Games
Beck, Justin D., Ejov, Vladimir and Filar, Jerzy A. (2012). Incompetence and impact of training in Bimatrix Games. Automatica, 48 (10), 2400-2408. doi: 10.1016/j.automatica.2012.06.046
Constraint augmentation in pseudo-singularly perturbed linear programs
Avrachenkov, K., Burachik, R. S., Filar, J. A. and Gaitsgory, V. (2012). Constraint augmentation in pseudo-singularly perturbed linear programs. Mathematical Programming, 132 (1-2), 179-208. doi: 10.1007/s10107-010-0388-0
A hybrid simulation-optimization algorithm for the Hamiltonian cycle problem
Eshragh, Ali, Filar, Jerzy A. and Haythorpe, Michael (2011). A hybrid simulation-optimization algorithm for the Hamiltonian cycle problem. Annals of Operations Research, 189 (1), 103-125. doi: 10.1007/s10479-009-0565-9
A Projection-Adapted Cross Entropy (PACE) method for transmission network planning
Eshragh, Ali, Filar, Jerzy A. and Nazari, Asef (2011). A Projection-Adapted Cross Entropy (PACE) method for transmission network planning. Energy Systems, 2 (2), 189-208. doi: 10.1007/s12667-011-0033-x
Hamiltonian Cycles, Random Walks, and Discounted Occupational Measures
Esragh, Ali and Filar, Jerzy A. (2011). Hamiltonian Cycles, Random Walks, and Discounted Occupational Measures. Mathematics of Operations Research, 36 (2), 258-270. doi: 10.1287/moor.1110.0492
Multivariate polynomial perturbations of algebraic equations
Avrachenkov, K., Ejov, V. and Filar, J. A. (2010). Multivariate polynomial perturbations of algebraic equations. Journal of Mathematical Analysis and Applications, 369 (1), 214-221. doi: 10.1016/j.jmaa.2010.02.026
A conjecture on the prevalence of cubic bridge graphs
Filar, Jerzy A. , Haythorpe, Michael and Nguyen, Giang T. (2010). A conjecture on the prevalence of cubic bridge graphs. Discussiones Mathematicae: Graph Theory, 30 (1), 175-179. doi: 10.7151/dmgt.1485
A note on price volatility in the Australian electricity market
Conticini, Celia, Filar, Jerzy A. and Nazari, Asef (2010). A note on price volatility in the Australian electricity market. ANZIAM Journal, 51, C730-C746. doi: 10.21914/anziamj.v51i0.2655
Environmental problems, uncertainty and mathematical modeling
Boland, John W., Filar, Jerzy A. and Howlett, Phil G. (2010). Environmental problems, uncertainty and mathematical modeling. Notices of the American Mathematical Society, 57 (10), 1286-1294.
Refined MDP-Based Branch-and-Fix Algorithm for the Hamiltonian Cycle Problem
Ejov, Vladimir, Filar, Jerzy A., Haythorpe, Michael and Nguyen, Giang T. (2009). Refined MDP-Based Branch-and-Fix Algorithm for the Hamiltonian Cycle Problem. Mathematics of Operations Research, 34 (3), 758-768. doi: 10.1287/moor.1090.0398
On the Hamiltonicity Gap and doubly stochastic matrices
Borkar, Vivek S., Ejov, Vladimir and Filar, Jerzy A. (2009). On the Hamiltonicity Gap and doubly stochastic matrices. Random Structures and Algorithms, 34 (4), 502-519. doi: 10.1002/rsa.20237
Determinants and longest cycles of graphs
Ejov, Vladimir, Filar, Jerzy A. , Murray, Walter and Nguyen, Giang T. (2008). Determinants and longest cycles of graphs. SIAM Journal on Discrete Mathematics, 22 (3), 1215-1225. doi: 10.1137/070693898
On regularly perturbed fundamental matrices
Ejov, Vladimir, Filar, Jerzy A. and Spieksma, Flora M. (2007). On regularly perturbed fundamental matrices. Journal of Mathematical Analysis and Applications, 336 (1), 18-30. doi: 10.1016/j.jmaa.2007.01.107
Clustering of spectra and fractals of regular graphs
Ejov, V., Filar, J. A., Lucas, S. K. and Zograf, P. (2007). Clustering of spectra and fractals of regular graphs. Journal of Mathematical Analysis and Applications, 333 (1), 236-246. doi: 10.1016/j.jmaa.2006.09.072
A Model for Adaptive Rescheduling of Flights in Emergencies (MARFE)
Filar, Jerzy A., Manyem, Prabhu, Panton, David M. and White, Kevin (2007). A Model for Adaptive Rescheduling of Flights in Emergencies (MARFE). Journal of Industrial and Management Optimization, 3 (2), 335-356.
Controlled Markov Chains, Graphs and Hamiltonicity
Filar, J. A. (2007). Controlled Markov Chains, Graphs and Hamiltonicity. Foundations and Trends in Stochastic Systems, 1 (2), 77-162. doi: 10.1561/0900000003
Solving the Hamiltonian Cycle Problem using symbolic determinants
Ejov, V, Filar, JA, Lucas, SK and Nelson, JL (2006). Solving the Hamiltonian Cycle Problem using symbolic determinants. Taiwanese Journal of Mathematics, 10 (2), 327-338.
Gröbner bases in asymptotic analysis of perturbed polynomial programs
Ejov, Vladimir and Filar, Jerzy A. (2006). Gröbner bases in asymptotic analysis of perturbed polynomial programs. Mathematical Methods of Operations Research, 64 (1), 1-16. doi: 10.1007/s00186-006-0073-5
On Newton's Polygons, Groebner Bases and Series Expansions of Perturbed Polynomial Programs
Avrachenkov, K., Ejov, V. and Filar, J. A. (2006). On Newton's Polygons, Groebner Bases and Series Expansions of Perturbed Polynomial Programs. Banach Center Publications, 71 (1), 29-38. doi: 10.4064/bc71-0-2
Time consistent dynamic risk measures
Boda, K and Filar, JA (2006). Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63 (1), 169-186. doi: 10.1007/s00186-005-0045-1
Connected co-spectral graphs are not necessarily both Hamiltonian
Filar, J. A., Gupta, A. and Lucas, S. K. (2005). Connected co-spectral graphs are not necessarily both Hamiltonian. Australian Mathematical Society Gazette, 32 (3), 193-193.
Weighted singularly perturbed hybrid stochastic systems
Liu, Ke and Filar, Jerzy A. (2005). Weighted singularly perturbed hybrid stochastic systems. Mathematical Methods of Operations Research, 62 (1), 41-54. doi: 10.1007/s00186-005-0440-7
Heroin users in Australia: population trends
Kaya, C.Y., Tugai, Y., Filar, J.A., Agrawal, M.R., Ali, R.L., Gowing, L.R. and Cooke, R. (2004). Heroin users in Australia: population trends. Drug and Alcohol Review, 23 (1), 107-116. doi: 10.1080/09595230410001645600
Stochastic target hitting time and the problem of early retirement
Boda, K, Filar, JA, Lin, YL and Spanjers, L (2004). Stochastic target hitting time and the problem of early retirement. Ieee Transactions On Automatic Control, 49 (3), 409-419. doi: 10.1009/TAC.2004.824469
Directed graphs, hamiltonicity and doubly stochastic matrices
Borkar, VS, Ejov, V and Filar, JA (2004). Directed graphs, hamiltonicity and doubly stochastic matrices. Random Structures and Algorithms, 25 (4), 376-395. doi: 10.1002/rsa.20034
Hamiltonian cycles and singularly perturbed Markov chains
Ejov, V, Filar, JA and Nguyen, MT (2004). Hamiltonian cycles and singularly perturbed Markov chains. Mathematics of Operations Research, 29 (1), 114-131. doi: 10.1287/moor.1030.0066
Environmental Assessment Based on Multiple Indicators
Filar, J. A., Ross, N. P. and Wu, M-L. (2003). Environmental Assessment Based on Multiple Indicators. Calcutta Statistical Association Bulletin, 54 (1-2), 93-104. doi: 10.1177/0008068320030108
Ejov, Vladimir, Filar, Jerzy A. and Thredgold, Jane (2003). Geometric interpretation of Hamiltonian cycles problem via singularly perturbed Markov decision processes. Optimization, 52 (4-5), 441-458. doi: 10.1080/02331930310001611529
An asymptotic simplex method for singularly perturbed linear programs
Filar, Jerzy A., Altman, Eitan and Avrachenkov, Konstantin E. (2002). An asymptotic simplex method for singularly perturbed linear programs. Operations Research Letters, 30 (5), 295-307. doi: 10.1016/S0167-6377(02)00152-9
Cesaro limits of analytically perturbed stochastic matrices
Filar, J, Krieger, HA and Syed, Z (2002). Cesaro limits of analytically perturbed stochastic matrices. Linear Algebra and its Applications, 353 (1-3), 227-243. doi: 10.1016/S0024-3795(02)00308-7
How airlines and airports recover from perturbations: a survey
Filar, Jerzy A. , Manyem, Prabhu and White, Kevin (2001). How airlines and airports recover from perturbations: a survey. Annals of Operations Research, 108 (1), 315-333. doi: 10.1023/A:1016079600083
A two-factor stochastic production model with two time scales
Filar, J. A. and Haurie, A. (2001). A two-factor stochastic production model with two time scales. Automatica, 37 (10), 1505-1513. doi: 10.1016/S0005-1098(01)00123-6
Weighted Markov decision processes with perturbation
Liu, K and Filar, JA (2001). Weighted Markov decision processes with perturbation. Mathematical Methods of Operations Research, 53 (3), 465-480. doi: 10.1007/s001860100125
Control of singularly perturbed hybrid stochastic systems
Filar, Jerzy A., Gaitsgory, Vladimir and Haurie, Alain B. (2001). Control of singularly perturbed hybrid stochastic systems. Ieee Transactions On Automatic Control, 46 (2), 179-190. doi: 10.1109/9.905686
Asymptotic analysis of perturbed mathematical programs
Coulomb, JM, Filar, JA and Szczechla, W (2000). Asymptotic analysis of perturbed mathematical programs. Journal of Mathematical Analysis and Applications, 251 (1), 132-156. doi: 10.1006/jmaa.2000.7025
Semi-infinite Markov decision processes
Chen, M, Filar, JA and Liu, K (2000). Semi-infinite Markov decision processes. Mathematical Methods of Operations Research, 51 (1), 115-137. doi: 10.1007/s001860050006
A non-standard branch and bound method for the Hamiltonian cycle problem
Filar, J. A. and Lasserre, Jean B. (2000). A non-standard branch and bound method for the Hamiltonian cycle problem. ANZIAM Journal, 42 (E), C586-C607. doi: 10.21914/anziamj.v42i0.614
Dynamic Cooperative Game Theory
Filar, Jerzy A. and Petrosjan, Leon A. (2000). Dynamic Cooperative Game Theory. International Game Theory Review, 2 (1), 47-65. doi: 10.1142/S0219198900000044
Shi, Peng and Filar, Jerzy A. (2000). Stability analysis and controller design for a class of uncertain systems with Markovian jump parameters. IMA Journal of Mathematical Control and Information, 17 (2), 179-190. doi: 10.1093/imamci/17.2.179
Altman, E, Avrachenkov, KE and Filar, JA (1999). Asymptotic linear programming and policy improvement for singularly perturbed Markov decision processes. Mathematical Methods of Operations Research, 49 (1), 97-109.
Uncertainty propagation within an integrated model of climate change
Zapert, R, Gaertner, PS and Filar, JA (1998). Uncertainty propagation within an integrated model of climate change. Energy Economics, 20 (5-6), 571-598. doi: 10.1016/S0140-9883(98)00014-0
On the Puiseux series expansion of the limit discount equation of stochastic games
Szczechla, WW, Connell, SA, Filar, JA and Vrieze, OJ (1997). On the Puiseux series expansion of the limit discount equation of stochastic games. Siam Journal On Control and Optimization, 35 (3), 860-875. doi: 10.1137/S0363012995284138
A regional allocation of world CO2 emission reductions
Filar, JA and Gaertner, PS (1997). A regional allocation of world CO2 emission reductions. Mathematics and Computers in Simulation, 43 (3-6), 269-275. doi: 10.1016/S0378-4754(97)00009-8
Algorithms for singularly perturbed markov control problems: a survey
Abbad, M. and Filar, J. A. (1995). Algorithms for singularly perturbed markov control problems: a survey. Control and Dynamic Systems, 73, 257-286.
Percentile performance criteria for limiting average Markov decision processes
Filar, Jerzy A., Krass, Dmitry and Ross, Keith W. (1995). Percentile performance criteria for limiting average Markov decision processes. IEEE Transactions on Automatic Control, 40 (1), 2-10. doi: 10.1109/9.362904
Stochasticity in the image greenhouse model
Braddock R.D., Filar J.A. and Zapert R. (1995). Stochasticity in the image greenhouse model. Mathematical and Computer Modelling, 22 (10-12), 15-25. doi: 10.1016/0895-7177(95)00176-3
Hamiltonian Cycles and Markov-Chains
Filar, JA and Krass, D (1994). Hamiltonian Cycles and Markov-Chains. Mathematics of Operations Research, 19 (1), 223-237. doi: 10.1287/moor.19.1.223
Filar J.A., Nickerson D.J. and Ross N.P. (1994). Inspection optimization model. Socio-Economic Planning Sciences, 28 (3), 137-146. doi: 10.1016/0038-0121(94)90001-9
The image greenhouse model as a mathematical system
Braddock R., Filar J., Zapert R., Rotmans J. and den Elzen M. (1994). The image greenhouse model as a mathematical system. Applied Mathematical Modelling, 18 (5), 234-254. doi: 10.1016/0307-904X(94)90332-8
A Weighted Markov Decision-Process
Krass, D, Filar, JA and Sinha, SS (1992). A Weighted Markov Decision-Process. Operations Research, 40 (6), 1180-1187. doi: 10.1287/opre.40.6.1180
Algorithms for singularly perturbed limiting average markov control problems
Abbad M., Filar J.A. and Bielecki T.R. (1992). Algorithms for singularly perturbed limiting average markov control problems. Ieee Transactions On Automatic Control, 37 (9), 1421-1425. doi: 10.1109/9.159585
Perturbation and stability theory for markov control problems
Abbad M. and Filar J.A. (1992). Perturbation and stability theory for markov control problems. Ieee Transactions On Automatic Control, 37 (9), 1415-1420. doi: 10.1109/9.159584
Some comments on a theorem of Hardy and Littlewood
Sznajder R. and Filar J.A. (1992). Some comments on a theorem of Hardy and Littlewood. Journal of Optimization Theory and Applications, 75 (1), 201-208. doi: 10.1007/BF00939913
Weighted reward criteria in Competitive Markov Decision Processes
Filar J.A. and Vrieze O.J. (1992). Weighted reward criteria in Competitive Markov Decision Processes. ZOR Zeitschrift f�r Operations Research Methods and Models of Operations Research, 36 (4), 343-358. doi: 10.1007/BF01416234
Algorithms for stochastic games - A survey
Raghavan T.E.S. and Filar J.A. (1991). Algorithms for stochastic games - A survey. ZOR Zeitschrift f�r Operations Research Methods and Models of Operations Research, 35 (6), 437-472. doi: 10.1007/BF01415989
Nonlinear programming and stationary equilibria in stochastic games
Filar, J. A., Schultz, T. A., Thuijsman, F. and Vrieze, O. J. (1991). Nonlinear programming and stationary equilibria in stochastic games. Mathematical Programming, 50 (1-3), 227-237. doi: 10.1007/BF01594936
Singularly perturbed Markov control problem: Limiting average cost
Bielecki T.R. and Filar J.A. (1991). Singularly perturbed Markov control problem: Limiting average cost. Annals of Operations Research, 28 (1), 153-168. doi: 10.1007/BF02055579
Variance-penalized Markov decision process
Filar, Jerzy A., Kallenberg, L. C. M. and Lee, Huey-Miin (1989). Variance-penalized Markov decision process. Mathematics of Operations Research, 14 (1), 147-161. doi: 10.1287/moor.14.1.147
Communicating MDPs: Equivalence and LP properties
Filar J.A. and Schultz T.A. (1988). Communicating MDPs: Equivalence and LP properties. Operations Research Letters, 7 (6), 303-307. doi: 10.1016/0167-6377(88)90062-4
Bilinear programming and structured stochastic games
Filar J.A. and Schultz T.A. (1987). Bilinear programming and structured stochastic games. Journal of Optimization Theory and Applications, 53 (1), 85-104. doi: 10.1007/BF00938818
Multiobjective Markov Decision-Process with Average Reward Criterion
Durinovic, S, Lee, HM, Katehakis, MN and Filar, JA (1986). Multiobjective Markov Decision-Process with Average Reward Criterion. Large Scale Systems in Information and Decision Technologies, 10 (3), 215-226.
Nonlinear programming and stationary strategies in stochastic games
Filar J.A. and Schultz T.A. (1986). Nonlinear programming and stationary strategies in stochastic games. Mathematical Programming, 34 (2), 243-247. doi: 10.1007/BF01580590
On the computation of equilibria in discounted Stochastic games
Breton, Michele, Haurie, Alain and Filar, Jerzy A. (1986). On the computation of equilibria in discounted Stochastic games. Journal of Economic Dynamics and Control, 10 (1-2), 33-36. doi: 10.1016/0165-1889(86)90013-8
Quadratic programming and the single-controller stochastic game
Filar J.A. (1986). Quadratic programming and the single-controller stochastic game. Journal of Mathematical Analysis and Applications, 113 (1), 136-147. doi: 10.1016/0022-247X(86)90338-0
Filar, JA and Schultz, TA (1986). The Traveling Inspector Model. Or Spektrum, 8 (1), 33-36.
The Completely Mixed Single-Controller Stochastic Game
Filar, JA (1985). The Completely Mixed Single-Controller Stochastic Game. Proceedings of the American Mathematical Society, 95 (4), 585-594. doi: 10.2307/2045849
Player Aggregation in the Traveling Inspector Model
Filar J.A. (1985). Player Aggregation in the Traveling Inspector Model. Ieee Transactions On Automatic Control, 30 (8), 723-729. doi: 10.1109/TAC.1985.1104060
A Matrix Game Solution of the Single-Controller Stochastic Game
Filar, JA and Raghavan, Tes (1984). A Matrix Game Solution of the Single-Controller Stochastic Game. Mathematics of Operations Research, 9 (3), 356-362. doi: 10.1287/moor.9.3.356
On stationary equilibria of a single-controller stochastic game
Filar J.A. (1984). On stationary equilibria of a single-controller stochastic game. Mathematical Programming, 30 (3), 313-325. doi: 10.1007/BF02591936
Semi-Antagonistic Equilibrium Points and Action Costs
Filar, J. A. (1984). Semi-Antagonistic Equilibrium Points and Action Costs. Cahiers Du Centre D'Etudes De Recherche Operationelle, 26 (3-4), 227-239.
A Finite Algorithm for the Switching Control Stochastic Game
Vrieze, OJ, Tijs, SH, Raghavan, Tes and Filar, JA (1983). A Finite Algorithm for the Switching Control Stochastic Game. Or Spektrum, 5 (1), 15-24.
Percentiles and Markov Decision Processes
Filar, Jerzy A. (1983). Percentiles and Markov Decision Processes. Operations Research Letters, 2 (1), 13-15. doi: 10.1016/0167-6377(83)90057-3
An Algorithm for Solving S-Games and Differential S-Games
Filar, JA and Raghavan, Tes (1982). An Algorithm for Solving S-Games and Differential S-Games. Siam Journal On Control and Optimization, 20 (6), 763-769. doi: 10.1137/0320055
A single loop stochastic game which one player can terminate
Filar, J. A. (1981). A single loop stochastic game which one player can terminate. OPSEARCH, 18 (4), 185-203.
Filar J.A. (1981). Ordered field property for stochastic games when the player who controls transitions changes from state to state. Journal of Optimization Theory and Applications, 34 (4), 503-515. doi: 10.1007/BF00935890
Estimation of Strategies in a Markov Game
Filar, JA (1976). Estimation of Strategies in a Markov Game. Naval Research Logistics, 23 (3), 469-480. doi: 10.1002/nav.3800230309
Mendiolar, Manuela, Filar, Jerzy A., O'Neill, Michael F., Martin, Tyson, Teixeira, Daniella, Webley, James and Holden, Matthew (2023). Estimating recreational catch. 25th International Congress on Modelling and Simulation, Darwin, NT Australia, 9 to 14 July 2023. Canberra, ACT Australia: Modelling and Simulation Society of Australia and New Zealand. doi: 10.36334/modsim.2023.mendiolar
POMDPs for sustainable fishery management
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
Size does matter: A simulation study of hospital size and operational efficiency
Bogomolov, T., Filar, J. A., Luscombe, R., Nazarathy, Y., Qin, S., Swierkowski, P. and Wood, I. (2017). Size does matter: A simulation study of hospital size and operational efficiency. 22nd International Congress on Modelling and Simulation, Hobart, TAS Australia, 3 - 8 December 2017. Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ).
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
Hospital Event Simulation Model: Arrivals to Discharge
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.
Markov decision process model for optimisation of patient flow
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.
An Interior point heuristic for the hamiltonian cycle problem via markov decision processes
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. DORDRECHT: SPRINGER. doi: 10.1023/B:JOGO.0000044772.11089.1a
Decomposition and parallel processing techniques for two-time scale controlled Markov chains
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
Optimal Ergodic Control of Singularly Perturbed Hybrid Stochastic Systems
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.
An application of optimization to the problem of climate change
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.
Asymptotic Analysis of a Stochastic Manufacturing System with Slow and Fast Motions
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.
Perturbation theory for semi-Markov control problems
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.
Percentile objective criteria in limiting average Markov control problems
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
The Embedding of the Traveling Salesman Problem in a Markov Decision Process
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
Gain/variability tradeoffs in undiscounted Markov decision processes
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
Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries
(2021–2024) ARC Discovery Projects
Modelling environmental changes and effects on wild-caught species in Queensland
(2019–2021) Fisheries Research & Development Corporation
Time Consistency, Risk-Mitigation and Partially Observable Systems
(2018–2023) ARC Discovery Projects
(2018–2019) Queensland Department of Agriculture and Fisheries
(2017–2018) Flinders University
(2016–2019) ARC Discovery Projects
(2016–2018) Flinders University
Application of machine learning in sustainable fisheries assessment and management
Doctor Philosophy — Associate Advisor
Other advisors:
Parametric sensitivity of threshold risk and multi-absorption phase type distributions
(2024) Doctor Philosophy — Principal Advisor
Other advisors:
Evolutionary games under incompetence & foraging strategies of marine bacteria
(2019) Doctor Philosophy — Principal Advisor
On quantitative indices and modelling of harvested fish populations
(2024) Doctor Philosophy — Associate Advisor
Other advisors:
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.
Risk and Uncertainty Quantification in Environmental Modelling
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.
Fishery-dependent monitoring of Queensland's fisheries
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.
Queensland state-wide estimation of recreational fish catches
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.