MATHECOEVO

Between evolutionary games and life history theory

Coordinatore	UNIVERSITY OF SUSSEX    more  Organization address address: Sussex House city: FALMER, BRIGHTON postcode: BN1 9RH contact info Titolo: Ms. Nome: Rossana Cognome: Dowsett Email: send email Telefono: +44 1273 678238 Fax: +44 1273 678192
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	165˙540 €
EC contributo	165˙540 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2009-IEF
Funding Scheme	MC-IEF
Anno di inizio	2010
Periodo (anno-mese-giorno)	2010-09-01   -   2012-08-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITY OF SUSSEX  Organization address address: Sussex House city: FALMER, BRIGHTON postcode: BN1 9RH contact info Titolo: Ms. Nome: Rossana Cognome: Dowsett Email: send email Telefono: +44 1273 678238 Fax: +44 1273 678192	UK (FALMER, BRIGHTON)	coordinator	165˙540.80

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 Obiettivo del progetto (Objective)

'The objective of this project is to develop mathematical structure that will be connection of evolutionary game paradigm with life history theory. In classical life history optimization models there are no interactions among individuals or density dependence: "Life history evolution usually ignores density and frequency dependence. The justification is convenience, not logic or realism" (Stearns 1992) On the other hand, in classical game theoretic models there is no age or stage structure. Payoff describes averaged lifetime activity of an individual, which can be found for example in Cressman 1992: "...an individual's strategy is fixed over lifetime or, alternatively, the life history of an individual is its strategy.". Objectives of the project can be divided into three branches: a) Development of method of decomposition of entire population into subgroups (multipopulation replicator dynamics). This generalization of standard replicator dynamics approach will allow to game theoretic modeling of structured populations, divided into subclasses (different species, sexes, age or stage classes). b) Description of the dynamics of turnover of individuals. Explicit consideration of births and deaths instead of fixed Malthusian parameter will allow to include tradeoffs between mortality and fecundity to dynamic game theoretic models c) Derivation of game theoretic payoff functions due to the methods of life history theory. This approach will allow to formulate crossover problems where life history traits and phenotypic may affect interactions among individuals. Classical optimization of life histories approach investigates how ecology affects evolution of life history parameters. New approach will allow to model how life history parameters affect ecology of a population by determining outcomes of interactions between individuals.'