Department of Mathematics
City, University of London
Northampton Square
London EC1V 0HB
I will be teaching the following
lecture course this year:
Optimization and Decision Making
EPM945
My main research interest is the application of game theory
to the mathematical modelling of biology, especially using the concept of the
Evolutionarily Stable Strategy (ESS). My work has generally been theoretical,
considering both purely mathematical work on the theory of games in an
evolutionary context and more specific problems when a particular animal
behaviour or trait is concerned. I have published and continue to publish work
in the three main areas listed below.
Mathematical models of evolution
This area involves the development of the general mathematical theory of
evolution, rather than any specific application. Early work considered Patterns
of Evolutionarily Stable Strategies; a theoretical investigation into the
coexistence of different ESSs in the important game class matrix games. A key
theme has been multi-player game theory. Most evolutionary game theory concerns
two-player games only; this work includes an extension of the theory to many
players. A second important theme has been the modelling of evolution on graphs,
where populations can possess a complex non-homogeneous structure. These two
area have been brought together in a new framework for more evolution on
structured populations, which I have developed with Jan Rychtar from the
University of North Carolina at Greensboro, which is a central area of my
current research.
Models of parasitism
I have modelled two major types of parasitic behaviour on birds. The first of
these involves the modelling of kleptoparasitism, where animals acquire food by
stealing from others. This work began with the development of a general
modelling framework for kleptoparasitism and considers the circumstances under
which it is best to steal food (or not) for different scenarios and varying
biological and mathematical assumptions. The second area is the modelling of
brood parasitism. Raising young can be costly, and some birds lay their eggs in
the nests of other birds to avoid this cost. Various models of both conspecific
and interspecific parasitism are considered. I am continuing to work on
kleptoparasitism in particular.
Evolutionary responses to predators and group living
A key priority for many animals is the avoidance of predators. Many such prey
animals choose to live in groups, and these two features are often strongly
related. I have considered two major types of interactions between prey and
predators, the first of these being antipredator vigilance in animal groups.
This an extension of a well-established theory involving trade-offs in time
spent watching for predators and foraging, into dynamic changes in group
structure and in spatial and positioning factors. The second is the
co-evolution of defence and signalling. Many animals possess high levels of
toxins to make them unpalatable to potential predators. Such defences are
invisible, so they are often also brightly coloured to signal these defences,
and success depends upon a sufficient number of conspecifics with these traits
for predators to learn to avoid them.
I would be interested in supervising research students in
any of the general areas described above. For more details on my research, look
at some of the publications from the list below.
Book
Broom,M. &
Rychtar,J. (2013). Game-Theoretical Models in Biology. Chapman and Hall.
The second edition of this book will
appear in August 2022.
Papers
1. Broom,M.,
Cannings,C. & Vickers,G.T. (1993) On the number of local maxima of a
constrained quadratic form. Proceedings of the Royal Society of London A 443
573-584.
2. Broom,M.,
Cannings,C. & Vickers,G.T. (1994) Sequential methods for generating
patterns of ESS's. Journal of Mathematical Biology 32 597-615.
3. Broom,M.,
Cannings,C. & Vickers,G.T. (1996) Patterns of ESS's. Adding pairs to an
ESS. Mathematical Biosciences 136 21-34.
4. Broom,M., Cannings,C. & Vickers,G.T. (1996) Choosing a Nest Site:
Contests and Catalysts. American Naturalist 147 1108-1114.
5. Broom,M., Cannings,C. & Vickers,G.T. (1996) Some examples of
multi-player game dynamics. Proceedings of the European Mathematical Genetics
Meeting, Padova University Press 105-114.
6. Broom,M.,
Cannings,C. & Vickers,G.T. (1997) Multi-player matrix games. Bulletin
of Mathematical Biology 59 931-952.
7. Broom,M.
Cannings,C. & Vickers,G.T. (1997) A sequential-arrivals model of
territory acquisition. Journal of Theoretical Biology 189 257-272.
8. Broom,M.
& Ruxton,G.D. (1998) Evolutionarily Stable Stealing: Game Theory
applied to Kleptoparasitism. Behavioral Ecology 9 397-403.
9. Broom,M.
& Ruxton,G.D. (1998) Modelling responses in vigilance rates to arrivals
to and departures from a group of foragers. IMA Journal of Mathematics Applied
in Medicine and Biology 15 387-400.
10. Broom,M. & Ruxton,G.D. (1999) A game theoretic model of
kleptoparasitism. Proceedings of BioMedSim 99 27-31.
11. Ruxton,G.D. & Broom,M. (1999) Evolution of kleptoparasitism as a war of
attrition. Journal of Evolutionary Biology 12 755-759.
12. Proctor,C.J.
& Broom,M. (2000) A spatial model of antipredator vigilance. IMA
Journal of Mathematics Applied in Medicine and Biology 17 75-93.
13. Broom,M.,
Cannings,C. & Vickers,G.T. (2000) Evolution in Knockout Contests: the
Fixed Strategy Case. Bulletin of Mathematical Biology 62 451-466.
14. Broom,M.
(2000) Patterns of ESSs: the Maximal Pattern Conjecture revisited. Journal
of Mathematical Biology 40 406-412.
15. Broom,M.
(2000) Bounds on the number of ESSs of a matrix game. Mathematical Biosciences
167 163-175.
16. Broom,M.,
Cannings,C., & Vickers,G.T. (2000) Evolution in Knockout Contests: the
Variable Strategy Case. Selection 1 5-21.
17. Broom,M.,
Cannings,C. & Vickers,G.T. (2000) A sequential-arrivals model of
territory acquisition II. Journal of Theoretical Biology 207 389-403.
18. Ruxton,G.D.,
Broom,M. & Colegrave,N. (2001) Are unusually coloured eggs a signal to
potential conspecific brood parasites? American Naturalist 157 451-458.
19. Broom,M. &
Ruxton,G.D. (2001) A model of dominance and resource division amongst a
group of animals of differing quality. Population Ecology 43 213-220.
20. Proctor,C.J.,
Broom,M. & Ruxton,G.D. (2001) Modelling antipredator vigilance and
flight response in group foragers when warning signals are ambiguous. Journal
of Theoretical Biology 211 409-417.
21. Broom,M. &
Ruxton,G.D. (2002) A Game Theoretic Approach to Conspecific Brood
Parasitism. Behavioral Ecology 13 321-327.
22. Broom,M.
(2002) Using game theory to model the evolution of information: an
illustrative game. Entropy 4 35-46.
23. Ward,A.J.W.,Hoare,D.J.,Couzin,I.D.,Broom,M.
& Krause,J. (2002) The effects of parasitism and body length on
positioning within wild fish shoals Journal of Animal Ecology 71 10-14.
24. Broom,M.
(2002) A unified model of dominance hierarchy formation and maintenance.
Journal of Theoretical Biology 219 63-72.
25. Broom,M. &
Cannings,C. (2002) Modelling dominance hierarchy formation as a
multi-player game. Journal of Theoretical Biology 219 397-413.
26. Ruxton,G.D.
& Broom,M. (2002) Intraspecific brood parasitism can increase the
number of eggs an individual lays in its own nest. Proceedings of the Royal
Society of London B 269 1989-1992.
27. Ward,A.J.W.,
Botham,M.S., Hoare,D.J., James,R., Broom,M., Godin,J.G.J. & Krause,J.
(2002) Association patterns and shoal fidelity in the three-spined
stickleback. Proceedings of the Royal Society of London B 269 2451-2455.
28. Proctor,C.J.,
Broom,M. & Ruxton,G.D. (2003) A communication-based spatial model of
antipredator vigilance. Journal of Theoretical Biology 220 123-137.
29. Broom,M & Ruxton,G.D.
(2003) Evolutionarily stable kleptoparasitism: consequences of different
prey types. Behavioral Ecology 14 23-33.
30. Broom,M. (2003) The
use of multiplayer game theory in the modeling of biological populations.
Comments on Theoretical Biology 8 103-123.
31. Broom,M.,
Tang,Q. & Waxman,D. (2003) Mathematical Analysis of a Model describing
Evolution of an Asexual Population in a Changing Environment. Mathematical
Biosciences 186 93-108.
32. Broom,M.,
Borries,C. & Koenig,A. (2004) Infanticide and infant defense by males-
modeling the conditions in primate multi-male groups. Journal of Theoretical
Biology 231 261-270.
33. Luther,R.M. &
Broom,M. (2004) Rapid convergence to an equilibrium state in
kleptoparasitic populations. Journal of Mathematical Biology 48 325-339.
34. Broom,M. &
Ruxton,G.D. (2004) A framework for modelling and analysing conspecific
brood parasitism. Journal of Mathematical Biology 48 529-544.
35. Broom,M.,
Luther,R.M. & Ruxton,G.D. (2004) Resistance is useless? - extensions to
the game theory of kleptoparasitism. Bulletin of Mathematical Biology 66
1645-1658.
36. Broom,M. &
Ruxton,G.D. (2005) You can run or you can hide: optimal strategies for
cryptic prey against pursuit predators. Behavioral Ecology 16 534-540.
37. Broom,M. (2005) Evolutionary
games with variable payoffs. Comptes Rendus Biologies 328 403-412.
38. Broom,M.,
Speed,M.P. & Ruxton,G.D. (2005) Evolutionarily stable investment in
secondary defences. Functional Ecology 19 836-843.
39. Ruxton,G.D.,
Fraser, C. & Broom,M. (2005) An evolutionarily stable joining policy
for group foragers. Behavioral Ecology 16 856-864.
40. Yates,G.E. &
Broom,M. (2005) A stochastic model of the distribution of unequal
competitors between resource patches. Journal of Theoretical Biology 237
227-237.
41. Speed,M.P.,
Ruxton,G.D. & Broom,M. (2006) Automimicry and the evolution of discrete
prey defences. Biological Journal of the Linnean Society 87 393-402.
42. Proctor,C.J.,
Broom,M. & Ruxton,G.D. (2006) Antipredator vigilance in birds:
modelling the `edge' effect. Mathematical Biosciences 199 79-96.
43. Broom,M.,
Speed,M.P. & Ruxton,G.D. (2006) Evolutionarily stable defence and
signalling of that defence. Journal of Theoretical Biology 242 32-43.
44. Jackson,A.L.,Beauchamp,G.,Broom,M.
& Ruxton,G.D. (2006) Evolution of anti-predator traits in response to a
flexible targeting strategy by predators. Proceedings of the Royal Society of
London B 273 1055-1062.
45. Fraser,C.P.,Ruxton,G.D.
& Broom,M. (2006) Public information and patch estimation for group
foragers: a re-evaluation of patch quitting strategies in a patchy environment.
Oikos 112 311-321.
46. Broom,M. &
Rychtar,J. (2007) The evolution of a kleptoparasitic system under adaptive
dynamics. Journal of Mathematical Biology 54 151-177.
47. Ellis,J.,Broom,M.
& Jones,S. (2007) Protein-RNA interactions: Structural analysis and
functional classes of Protein-RNA binding sites. Proteins: structure, function
and bioinformatics 66 903-911.
48. Broom,M.,Ruxton,G.D. & Speed,M.P. (2007) Evolutionarily Stable
Investment in Anti-predatory Defences and Aposematic Signalling. In:
Mathematical Modeling of Biological Systems, Volume II. A. Deutsch, R. Bravo de
la Parra, R. de Boer, O. Diekmann, P. Jagers, E. Kisdi, M. Kretzschmar, P.
Lansky and H. Metz (eds). Birkhauser, Boston, 37-48.
49. Luther,R.M.,
Broom,M. & Ruxton,G.D. (2007) Is food worth fighting for? ESS's in
mixed populations of kleptoparasites and foragers. Bulletin of Mathematical
Biology 69 1121-1146.
50. Broom,M.,Nouvellet,P.,Bacon,J.P.
& Waxman,D. (2007) Parameter-free testing of the shape of a probability
distribution. Biosystems 90 509-515.
51. Yates,G.E. &
Broom,M. (2007) A stochastic model of kleptoparasitism. Journal of
Theoretical Biology 248 480-489.
52. Ruxton,G.D.,
Speed, M.P. & Broom,M. (2007) The importance of initial protection of
conspicuous mutants for the coevolution of defense and aposematic signaling of
the defense: A modeling study. Evolution 61 2165-2174.
53. Langridge,K.V.,
Broom,M. & Osorio, D. (2007) Selective signalling by cuttlefish to
predators. Current Biology 17 R1044-R1045.
54. Broom,M.,
Ruxton,G.D. & Kilner,R.M. (2008) Host life history strategies and the
evolution of chick-killing by brood parasitic offspring. Behavioral Ecology 19
22-34.
55. Broom,M. &
Rychtar,J. (2008) An analysis of the fixation probability of a mutant on
special classes of non-directed graphs. Proc. Roy. Soc. London A 464 2609-2627.
56. Broom,M.,
Rychtar,J. & Sykes,C. (2008) The Evolution of Kleptoparasitism under
Adaptive Dynamics Without Restriction. Journal of Interdisciplinary Mathematics
11 479-494.
57. Broom,M.,
Luther,R.M., Ruxton,G.D. & Rychtar,J. (2008) A game-theoretic model of
kleptoparasitic behavior in polymorphic populations. Journal of Theoretical
Biology 255 81-91.
58. Harrison,M.D.
& Broom, M. (2009) A game-theoretic model of interspecific brood
parasitism with sequential decisions. Journal of Theoretical Biology 256
504-517.
59. Broom,M.
Rychtar,J. & Stadler,B. (2009) Evolutionary Dynamics on Small-Order
Graphs. Journal of Interdisciplinary Mathematics 12 129-140.
60. Broom,M.,
Luther,R.M. & Rychtar,J. (2009) A Hawk-Dove game in kleptoparasitic
populations. Journal of Combinatorics, Information and System Science 4
449-462.
61. Broom,M. &
Rychtar,J. (2009) A game theoretical model of kleptoparasitism with
incomplete information. Journal of Mathematical Biology 59 631-649.
62. Broom,M.,
Koenig,A. & Borries,C. (2009) Variation in dominance hierarchies among
group-living animals: modeling stability and the likelihood of coalitions.
Behavioral Ecology 20 844-855.
63. Kiss, I. Z., Broom, M., and Rafols, I. (2009) Can epidemic models describe
the diffusion of research topics across disciplines? In Birger Larsen and
Jacqueline Leta (Eds.) Proceedings of the 12th International Conference of the
International Society for Scientometrics and Informetrics, Rio de Janeiro.
64. Ruxton,G.D.,
Speed,M.P. & Broom,M. (2009) Identifying the ecological conditions that
select for intermediate levels of aposematic signalling. Evolutionary Ecology
23 491-501.
65. Broom,M. (2009) Balancing
risks and rewards: the logic of violence. Frontiers in Neuroscience Vol.3.
66. Kiss, I.Z.,
Broom, M., Craze, P. & Rafols, I. (2010) Can epidemic models describe the
diffusion of topics across disciplines?. Journal of Informetrics 4 74-82.
67. Broom,M.,
Crowe.M, Fitzgerald.M & Rychtar,J. (2010) The stochastic modelling of
kleptoparasitism using a Markov process. Journal of Theoretical Biology 264
266-272.
68. Broom,M.,
Hadjichrysanthou,C. & Rychtar,J. (2010) Evolutionary games on graphs
and the speed of the evolutionary process. Proceedings of the Royal Society A
466 1327-1346.
69. Broom,M.,
Higginson,A.D. & Ruxton,G.D. (2010) Optimal investment across different
aspects of anti-predator defences. Journal of Theoretical Biology 263 579-586.
70. Broom,M.,
Hadjichrysanthou,C., Rychtar,J. and Stadler,B. (2010) Two results on
evolutionary processes on general non-directed graphs. Proceedings of the Royal
Society A 466 2795-2798.
71. Broom,M.
& Rychtar,J. (2011) Kleptoparasitic melees - modelling food stealing
featuring contests with multiple individuals. Bulletin of Mathematical Biology
73 683-699.
72. Broom,M. &
Ruxton,G.D. (2011) Some mistakes go unpunished - the evolution of all or
nothing signalling. Evolution 65 2743-2749.
73. Hadjichrysanthou,C.,
Broom,M. & Rychtar,J. (2011) Evolutionary games on star graphs under
various updating rules. Dynamic Games and Applications 1 386-407.
74. Broom,M.,
Rychtar,J. & Stadler,B. (2011) Evolutionary dynamics on graphs - the
effect of graph structure and initial placement on mutant spread. Journal of
Statistical Theory and Practice 5 369-381.
75. Broom,M &
Voelkl, B. (2012) Two Measures of Effective Population Size for Graphs.
Evolution 66 1613-1623.
76. Barker,H.A.,
Broom,M. & Rychtar,J. (2012) A game theoretical model of
kleptoparasitism with strategic arrivals and departures of beetles at dung pats
Journal of Theoretical Biology 300 292-298.
77. Broom,M.
& Rychtar,J. (2012) A general framework for analysing multiplayer games
in networks using territorial interactions as a case study. Journal of
Theoretical Biology 302 70-80.
78. Hadjichrysanthou,C.
& Broom,M. (2012) When should animals share? Game theory applied to
kleptoparasitic populations with food sharing. Behavioral Ecology 23 977-991.
79. Broom,M.,
Hughes,R.N., Burrows,M. & Ruxton,G.D. (2012) Evolutionarily stable
sexual allocation by both stressed and unstressed potentially simultaneous
hermaphrodites within the same population. Journal of Theoretical Biology 309
96-102.
80. Hadjichrysanthou,C.,
Broom,M. & Kiss,I.Z. (2012) Approximating evolutionary dynamics on
networks using a neighbourhood configuration model. Journal of Theoretical
Biology 312 13-21.
81. Broom,M.
& Ruxton,G.D. (2012) Perceptual advertisement by the prey of stalking
or ambush predators. Journal of Theoretical Biology 315 9-16.
82. Argasinski,K.
& Broom,M. (2013) Ecological theatre and the evolutionary game: how
environmental and demographic factors determine payoffs in evolutionary games.
Journal of Mathematical Biology.
83. Broom,M. &
Ruxton,G.D. (2013) On the evolutionary stability of zero-cost
pooled-equilibrium signals. Journal of Theoretical Biology 323 69-75.
84. Broom,M. (2013) Interactions between searching predators and hidden prey in
Search Theory: A Game Theoretic Perspective Alpern S., Fokkink R., Gasieniec
L., Lindelauf R., Subrahmanian V.S. (eds) Springer.
85. Broom,M. &
Cannings,C. (2013) Evolution of a network with strategic link formation
governed by individual preferences. Journal of Theoretical Biology 335 160-168.
86. Argasinski,K.
& Broom,M. (2013) The nest site lottery: how selectively neutral
density dependent growth suppression induces frequency dependent selection.
Theoretical Population Biology 90 82-90.
87. Broom,M.,
Rychtar,J. & Sykes,D.G. (2013) The effect of information on payoffs in
kleptoparasitic interactions. Springer Proceedings in Mathematics and
Statistics 64 125-134.
88. Broom,M.,
Ruxton,G.D. & Schaefer,H.M. (2013) Signal verification can promote
reliable signalling. Proceedings of the Royal Society B 280 20131560.
89. Bruni,M.,
Broom,M. & Rychtar,J. (2014) Analysing territorial models on graphs.
Involve 7 129-149.
90. Teichmann,J., Broom,
M. & Alonso, E. (2014) The application of temporal difference learning
in optimal diet models. Journal of Theoretical Biology 340 11-16.
91. Teichmann,J.,
Broom,M. & Alonso, E. (2014) The evolutionary dynamics of aposematism:
a numerical analysis of co-evolution in finite populations. Mathematical
Modelling of Natural Phenomena 9 148-164.
92. Broom,M.,
Rychtar,J. & Sykes,D.G. (2014) Kleptoparasitic interactions under
asymmetric resource valuation. Mathematical Modelling of Natural Phenomena 9
138-147.
93. Broom,M.
& Rychtar,J. (2014) Asymmetric games in monomorphic and polymorphic
populations. Dynamic Games and Applications 4 391-406.
94. Broom,M. &
Cannings,C. (2015) Graphic deviation. Discrete Mathematics 338 701-711.
95. Broom,M,
Johanis,M. & Rychtar,J. (2015) The effect of fight cost structure on
fighting behaviour. Journal of Mathematical Biology 71 979-996.
96. Kura,K, Broom,M.
& Kandler,A. (2015) Modelling dominance hierarchies under winner and
loser effects. Bulletin of Mathematical Biology 77 927-952.
97. Broom,M,
Lafaye,C., Pattni,K. & Rychtar,J. (2015) A study of the dynamics of
multi-player games on small networks using territorial interactions. Journal of
Mathematical Biology 71 1551-1574.
98. Teichmann,
J., Alonso, E. & Broom, M. (2015) A reward-driven model of Darwinian
fitness, Proceedings of the 7th International Joint Conference on Computational
Intelligence (IJCCI 2015) - Volume 1: ECTA, pp. 174-179, Lisbon, Portugal,
Scitepress.
99. Pattni,K.,
Broom,M., Rychtar,J. & Silvers,A.J. (2015) Evolutionary graph theory
revisited: when is an evolutionary process equivalent to the Moran process?.
Proceedings of the Royal Society A 471 2015.0334.
100. Broom,M. &
Rychtar,J. (2016) A model of food stealing with asymmetric information.
Ecological Complexity 26 137-142.
101. Broom,M.,
Rychtar, J. & Spears-Gill,T. (2016) The game-theoretical model of using
insecticide-treated bed-nets to fight malaria. Applied Mathematics 7 852-860.
102. Raza,M.S. &
Broom,M. (2016) Survival analysis modelling with hidden censoring. Journal
of Statistical Theory and Practice 10 1-14.
103. Li,A.,
Broom,M., Du.J. & Wang,L. (2016) Evolutionary dynamics of general group
interactions in structured populations. Physical Review E 93 022407.
104. Broom,M.
& Rychtar,J. (2016) Nonlinear and Mulitplayer Evolutionary Games in
Advances in Dynamic and Evolutionary Games, Thuijsman,F., Wagener,F. (eds)
Birkhauser.
105. Kura,K.,
Broom,M. & Kandler,A. (2016) A game-theoretical winner and loser model
of dominance hierarchy formation. Bulletin of Mathematical Biology 78 1259-90.
106. Broom,M.
& Rychtar,J. (2016) Ideal cost-free distributions in structured
populations for general payoff functions. Dynamic Games and Applications
doi:10.1007/s13235-016-0204-4.
107. Broom,M.
& Rychtar,J. (2017) Evolutionary games with sequential decisions and
dollar auctions. Dynamic Games and Applications doi: 10.1007/s13235-016-0212-4.
108. Chawsheen,T.
& Broom,M. (2017) Seasonal Time-Series Modelling and Forecasting
Monthly Mean Temperature for Decision Making in the Kurdistan Region of Iraq.
Journal of Statistical Theory and Practice 11 604-633.
109. Argasinski,K.
& Broom,M. (2017) Evolutionary stability under limited population
growth. Eco-evolutionary feedbacks and replicator dynamics. Ecological
Complexity doi:10.1016/j.ecocom.2017.04.002.
110. Hadjichrysanthou,C., Broom,M. & Rychtar,J. (2017) Models
of kleptoparasitism on networks: the effect of population structure on food
stealing behaviour Journal of Mathematical Biology doi
10.1007/s00285-017-1177-7.
111. Broom,M.
& Cannings,C. (2017) Game theoretical modelling of a dynamically
evolving network I: general target sequences. Journal of Dynamics and Games
112. Pattni,K., Broom,M. &
Rychtar,J. (2017) Evolutionary dynamics and the evolution of multiplayer
cooperation in a subdivided population. Journal of Theoretical Biology 429
105-115.
113. Teichmann,J.,
Alonso,E. & Broom, M. (2017) Reinforcement learning is an effective
strategy to create phenotypic variation and a potential mechanism for the
initial evolution of learning. Proceedings of the Conference on Artificial
Evolution 2017.
114. Teichmann,J., Alonso,E.
& Broom, M. (2017) Reinforcement Learning as a model of aposematism.
Proceedings of the Conference on Artificial Evolution 2017.
115. Broom,M,
Johanis,M. & Rychtar,J. (2018) The effect of
fight cost structure on fighting behaviour involving simultaneous decisions and
variable investment levels. Journal of Mathematical Biology 76 457-482.
116. Spencer,R.
& Broom,M. (2018) A game-theoretical model of kleptoparasitic behaviour
in an urban gull (Laridae) population. Behavioral Ecology 29 60-78.
117. Broom,M.,
Collins,D., Vu,T.H. & Thomas,P. (2018) The Four Regions in Settlement
Space: A Game-Theoretical Approach to Investment Treaty Arbitration. Part I:
Modelling. Law, Probability and Risk 17 55-78.
118. Broom,M.,
Collins,D., Vu,T.H. & Thomas,P. (2018) The Four Regions in Settlement
Space: A Game-Theoretical Approach to Investment Treaty Arbitration. Part II:
Cases. Law, Probability and Risk 17 79-98.
119. Broom,M.
& Krivan,V. (2018) Biology and Evolutionary Games: in Tamer Basar,
Georges Zaccour, eds. Handbook of Dynamic Game Theory. Springer.
120. Argasinski,K.
& Broom,M. (2018) Interaction rates, vital rates, background fitness and
replicator dynamics: how to embed evolutionary game structure into realistic
population dynamics. Theory in Biosciences 137 33-50.
121. Pattni,K.,
Broom,M. & Rychtar,J (2018) Evolving multiplayer networks: modelling
the evolution of cooperation in a mobile population. Discrete and Continuous
Dynamical Systems B 23 1975-2004.
122. Broom, M., Pattni, K.
& Rychtar, J. (2018) Generalized Social Dilemmas: The Evolution of
Cooperation in Populations with Variable Group Size.
Bulletin of Mathematical Biology. doi:10.1007/s11538-018-00545-1.
123. Schimit, P.H.T.,
Pattni, K. & Broom, M. (2019) Dynamics of multiplayer games on complex
networks using territorial interactions. Physical Review E, 99(3).
doi:10.1103/physreve.99.032306.
124. Overton, C.E., Broom,
M., Hadjichrysanthou, C. & Sharkey, K.J. (2019) Methods for
approximating stochastic evolutionary dynamics on graphs. Journal of
Theoretical Biology, 468 45-59.
125. Wang, L., Ruxton,
G.D., Cornell, S.J., Speed, M.P. & Broom, M. (2019) A theory for
investment across defences triggered at different stages of a predator-prey
encounter. Journal of Theoretical Biology 473 9-19.
126. Broom,M., Cressman,R.,
& Krivan,V. (2019) Revisiting the `fallacy of averages’ in ecology:
Expected gain per unit time equals expected gain divided by expected time.
Journal of Theoretical Biology483 article 109993.
127. Erovenko,I.E.,
Bauer,J., Broom,M. Pattni,K & Rychtar,J. (2019) The effect of network
topology on optimal exploration strategies and the evolution of cooperation in
a mobile population. Proceedings of the Royal Society of London A 475 20190399.
128. Bauer,J., Broom,M.
& Alonso, E. (2019) The Stabilisation of Equilibria in Evolutionary
Game Dynamics through Mutation: Mutation Limits in Evolutionary Games.
Proceedings of the Royal Society of London A 475 20190355.
129. Broom,M.,
Erovenko,I.V., Rowell,J.T. & Rychtar,J. (2020) Models and measures of
animal aggregation and dispersal. Journal of Theoretical Biology 494 110002.
130. Cannings,C. & Broom,M. (2020) Game
theoretical modelling of a dynamically evolving network II: target sequences of
score 1. Journal of Dynamics and Games 7 37-64.
131. Varga,T., Garay,J.,
Rychtar,J. & Broom,M. (2020) A temporal model of territorial defence
with antagonistic interactions. theoretical Population Biology 135 15-35.
132. Bishop,D.T., Broom,M.
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133. Garay,J.,
Cressman,R., Xu,F., Broom,M., Csiszar & V., Mori, T. (2020) When
optimal foragers meet in a game theoretical conflict: A model of
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134. Broom,M. &
Krivan,V. (2020) Two-strategy games with time constraints on regular
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135. Broom,M., Erovenko,I.
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involving multiplayer interactions. Dynamic Games and Applications 11 270-293.
136. Sun.S,
Broom,M.,
Johanis,M & Rychtar,J (2021) A mathematical model of kin selection in
floral displays. Journal of Theoretical Biology 509,110470.
137. Argasiski,K &
Broom,M. (2021) Towards a replicator dynamics model of age structured
populations. Journal of Mathematical Biology 82 44.
138. Aizouk,R. &
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sequences and graph realization. Discrete and Continuous Dynamical Systems
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139. Scaramangas,A. &
Broom,M. (2022) Aposematic signalling in prey-predator systems: determining
evolutionarily stability when prey populations consist of a single species.
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