Atnaujinkite slapukų nuostatas

El. knyga: Game Theory in Biology: concepts and frontiers

(School of Mathematics, University of Bristol, UK), (Department of Zoology, Stockholm University, Sweden)

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

The principles of game theory apply to a wide range of topics in biology. This book presents the central concepts in evolutionary game theory and provides an authoritative and up-to-date account. The focus is on concepts that are important for biologists in their attempts to explain observations. This strong connection between concepts and applications is a recurrent theme throughout the book which incorporates recent and traditional ideas from animal psychology, neuroscience, and machine learning that provide a mechanistic basis for behaviours shown by players of a game. The approaches taken to modelling games often rest on idealized and unrealistic assumptions whose limitations and consequences are not always appreciated. The authors provide a novel reassessment of the field, highlighting how to overcome limitations and identifying future directions.

Game Theory in Biology is an advanced textbook suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of behavioural ecology and evolutionary biology. It will also be of relevance to a broader interdisciplinary audience including psychologists and neuroscientists.

Recenzijos

a valuable book * Andrey Zahariev, zb Math Open * Advanced textbook for graduate-level students and professional researchers presents the central concepts and modeling approaches in biological game theory, highlighting the connection between concepts and applications, the limitations of current models, and areas for future development. * Journal of Economic Literature (Volume 59, no. 1) * This is an excellent exposition of game theory as applied in biology, written by two of the leading lights in the field. The book is very well crafted, with illuminating explanations and a nicely balanced use of mathematics. It is intended to be an advanced-level textbook and reference for biological researchers current or future. But it will also intrigue researchers in economics who indulge in interdisciplinarity. Economists will find the stylethe use of mathematics, in particular highly congenial. . . I recommend this book unreservedly * Arthur Robson, Department of Economics, Simon Fraser University, Journal of Economic Literature * There is much to like about this book. From the outset, the models are motivated by biological systems and observations. That is, the authors are using evolutionary theory to answer the question "why is nature like that?" rather than asserting that "this is how nature should be." . . . It is rare to find a book that can be used effectively by both experts and neophytes, but this is one. Indeed, I can imagine teaching an upper-level undergraduate seminar with it, passing it to a new postdoctoral researcher who wants to learn the methods, and using it in my own research. Owning it will be a wise investment. * Marc Mangel, University of Bergen, The Quarterly Review of Biology * McNamara and Leimar succeed in producing a very accessible discussion of topics from which many nontheoretically inclined researchers would usually struggle to gain much insight... I cannot recommend this book more enthusiastically...a real gem, bursting with revolutionary insight. * Sasha R.X. Dall, Animal Behaviour * [ The authors] stimulate the reader to imagine beyond the traditional ways of doing game theory in biology...The exercises, provided at the end of the main chapters, are excellent for practicing the logic presented. * Chaitanya Gokhale & Arne Traulsen, ISBE Newsletter * McNamara and Leimar have contributed to game theory in evolutionary biology for almost as long as the approach has existed. Their new book is for those who really want to know the nuts and bolts of the techniques involved, and is driven by examples of actual topics of interest to evolutionary and behavioural ecologists. * Hanna Kokko, Trends in Ecology and Evolution * Game Theory in Biology is an advanced textbook suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of behavioural ecology and evolutionary biology. It will also be of relevance to a broader interdisciplinary audience including psychologists and neuroscientists. * MathSciNet *

Acknowledgements xi
1 Setting the Scene
1(12)
1.1 Introduction
1(1)
1.2 Frequency Dependence
2(4)
1.3 The Modelling Approach
6(1)
1.4 Scope of the Field and Challenges
7(2)
1.5 Approach in This Book
9(4)
2 Central Concepts
13(14)
2.1 Actions, States, and Strategies
13(3)
2.2 The Phenotypic Gambit
16(3)
2.3 Invasion Fitness
19(3)
2.4 Evolutionary Endpoints
22(1)
2.5 Fitness Proxies
23(2)
2.6 From Strategies to Individuals
25(2)
3 Standard Examples
27(36)
3.1 Contributing to the Common Benefit at a Cost
27(3)
3.2 Helping Others: The Prisoner's Dilemma Game
30(1)
3.3 The Tragedy of the Commons
31(1)
3.4 Biparental Care: The Parental Effort Game
32(4)
3.5 Contest Over a Resource: The Hawk-Dove Game
36(5)
3.6 The Evolution of Signalling: From Cue to Signal
41(3)
3.7 Coordination Games
44(1)
3.8 Produce Sons or Daughters? The Sex-Allocation Game
45(3)
3.9 Playing the Field
48(1)
3.10 Dispersal as a Means of Reducing Kin Competition
48(2)
3.11 Threshold Decisions
50(6)
3.12 Assessment and Bayesian Updating
56(4)
3.13 Exercises
60(3)
4 Stability Concepts: Beyond Nash Equilibria
63(28)
4.1 Evolutionarily Stable Strategies
64(5)
4.2 Adaptive Dynamics
69(5)
4.3 Evolution to a Fitness Minimum
74(5)
4.4 Replicator Dynamics
79(2)
4.5 Games Between Relatives
81(6)
4.6 Exercises
87(4)
5 Learning in Large Worlds
91(20)
5.1 Reinforcement Learning
92(4)
5.2 Learning and the Hawk-Dove Game
96(3)
5.3 Learning in a Game of Joint Benefit of Investment
99(3)
5.4 A Dominance Game
102(5)
5.5 Approaches to Learning in Game Theory
107(2)
5.6 Exercises
109(2)
6 Co-evolution of Traits
111(30)
6.1 Stability in More than One Dimension
112(2)
6.2 Role Asymmetries
114(4)
6.3 The Evolution of Anisogamy
118(4)
6.4 Evolution of Abilities and Role Specialization
122(4)
6.5 Learning and Individual Specialization
126(4)
6.6 Co-evolution of Prosociality and Dispersal
130(3)
6.7 Co-evolution of Species
133(4)
6.8 Concluding Comments
137(1)
6.9 Exercises
138(3)
7 Variation, Consistency, and Reputation
141(32)
7.1 Variation has Consequences
141(2)
7.2 Variation and the Stability of Equilibria
143(2)
7.3 Taking a Chance
145(3)
7.4 Signalling and the Handicap Principle
148(2)
7.5 Reputation
150(3)
7.6 Indirect Reciprocity
153(4)
7.7 Differences Select for Social Sensitivity
157(3)
7.8 Markets
160(4)
7.9 Choosiness, Assortment, and Cooperation
164(2)
7.10 Commitment
166(3)
7.11 Exercises
169(4)
8 Interaction, Negotiation, and Learning
173(30)
8.1 Interaction over Time
173(1)
8.2 Information and the Order of Choice
174(3)
8.3 Credible Threats and Strategic Commitment
177(3)
8.4 Negotiation between Partners
180(5)
8.5 Evolution of Cognitive Bias
185(3)
8.6 Social Dominance
188(5)
8.7 Assessment in Contests
193(5)
8.8 Outlook: Games with Interaction over Time
198(3)
8.9 Exercises
201(2)
9 Games Embedded in Life
203(28)
9.1 Self-consistency
203(1)
9.2 The Shadow of the Future, and the Past
204(2)
9.3 Resident Strategy Affects Future Opportunities
206(3)
9.4 Dependence on Future Actions
209(7)
9.5 Territorial Defence and the Desperado Effect
216(5)
9.6 State-dependent Ideal Free Distributions
221(5)
9.7 Is it Worth it?
226(2)
9.8 Exercises
228(3)
10 Structured Populations and Games over Generations
231(30)
10.1 Invasion Fitness for Structured Populations
233(3)
10.2 Offspring Quality versus Number
236(4)
10.3 Reproductive Value Maximization
240(2)
10.4 Sex Allocation as a Game over Generations
242(4)
10.5 The Fisher Runaway Process
246(6)
10.6 Maximizing Lifetime Reproductive Success
252(3)
10.7 Dispersal
255(2)
10.8 Evolutionary Analysis in Structured Populations
257(1)
10.9 Exercises
258(3)
11 Future Perspectives
261(12)
11.1 Phylogeny
263(3)
11.2 Behavioural Mechanisms in Large Worlds
266(5)
11.3 Ontogeny and the Acquisition of Behaviour
271(2)
Appendix A Summary of Notation 273(2)
Appendix B Solutions to Exercises 275(30)
References 305(20)
Index 325
John McNamara is Emeritus Professor of Mathematics and Biology at the University of Bristol, UK. After competing his doctorate on black holes in 1976, he changed his research focus to animal behaviour and evolutionary biology. In these fields he has developed approaches to modelling behaviour, particularly approaches based on state variables. In studying animal behaviour his objective has been to provide theoretical explanations of known phenomena and to motivate and steer the direction of new experiments. Areas in which he has contributed include foraging theory, life history theory, and game theory.

Olof Leimar is Emeritus Professor of Zoology at Stockholm University, Sweden. After studying theoretical physics in Stockholm, he switched to biology and completed his doctorate in 1988 with a thesis on game-theory analysis of animal fighting. In modelling animal behaviour, he introduces behavioural mechanisms, including mechanisms from learning psychology to achieve greater biological realism. In addition to fighting behaviour, he has applied this approach to the evolution of warning colouration and mimicry. Other fields he has worked in include sex allocation, mutualism, life-history theory, developmental plasticity and phenotype determination. He develops mathematical models, but he has also been involved in experimental work in his areas of interest.