1. Ph.D proposal steven hamblin

2. +=? 3. Social foragingEquilibrium behaviourLearning 4. Producer 5. Scrounger Producers 6. Chapter 1: Evolution of learning rules for foraging. 7. Chapter 1: Evolution of learning rules for foraging. Chapter 2: Learning rules, the next step. 8. Chapter 1: Evolution of learning rules for foraging. Chapter 2: Learning rules, the next step. Chapter 3: Landscape geometry and foraging. 9. Chapter 1: Evolution of learning rules for foraging. Chapter 2: Learning rules, the next step. Chapter 3: Landscape geometry and foraging. Chapter 4: Predator-prey coevolution. 10. Chapter 1: Evolution of learning rules. 11. Scrounger Producers 12. Rules 13. Rules Relative payoff sum 14. Rules Relative payoff sum Perfect Memory 15. Rules Relative payoff sum Perfect Memory Linear Operator 16. Relative Payoff Sum?Si (t) = xSi (t1) + (1Perfect Memory?Si (t) =Linear Operator?Si (t) = xSi (tx)ri + Pi (t)+ Ri (t)/( + Ni (t))1) + (1x)Pi (t) 17. Relative Payoff Sum?Si (t) = xSi (t1) + (1Perfect Memory?Si (t) =Linear Operator?Si (t) = xSi (tx)ri + Pi (t)+ Ri (t)/( + Ni (t))1) + (1x)Pi (t) 18. Relative Payoff Sum?Si (t) = xSi (t1) + (1Perfect Memory?Si (t) =Linear Operator?Si (t) = xSi (tx)ri + Pi (t)+ Ri (t)/( + Ni (t))1) + (1x)Pi (t)Multiple stable rules with multiple parameters? 19. Perfect Memory? Relative Payoff Sum?Linear Operator? 20. Agent StartProduce or scrounge?ProduceAt a patch with food?ScroungeNoMove randomlyNoAny conspecics feeding?YesFeedNoMove to closestStill food in patch?YesThere yet?YesNoFeedClosest still feeding?YesNo 21. Foraging grid is a variable-sized square grid with movement in the 4 cardinal directions.Agent StartProduce or scrounge?ProduceAt a patch with food?ScroungeNoMove randomlyNoNumber of patches and number of agents kept to 20% and 10% of grid size.Any conspecics feeding?YesFeedNoMove to closestStill food in patch?YesThere yet?YesNoFeedClosest still feeding?YesNoThus: 40x40 grid would have 320 patches and 160 agents 22. Genetic AlgorithmsAlgorithms that simulate evolution to solve optimization problems. 23. Initial populationMeasure tness> n generationsSelect for reproductionMutationExit 24. Genetic algorithm to optimize parameters and simulate population dynamics.Foraging / Learning rule simulation. 25. Chapter 2: Learning rules, the next step. 26. Si (t) = xSi (t1) + (1Perfect Memory? Si (t) = xSi (tx)ri + Pi (t)Si (t) =1) + (1Relative Payoff Sum?+ Ri (t)/( + Ni (t))x)Pi (t)Linear Operator? 27. Initial populationMeasure tness> n generationsSelect for reproductionMutationExit 28. Genetic algorithm to optimize parameters and simulate population dynamics.Foraging / Learning rule simulation. 29. Genetic programming to optimize rule structure.Genetic algorithm to optimize parameters and simulate population dynamics.Foraging / Learning rule simulation. 30. Chapter 3: Landscape geometry and foraging. 31. + 32. Foraging simulation. 33. Foraging simulation.Swappable grids (Moore / VN / Hex / Dirichlet) 34. Genetic algorithm to optimize parameters and simulate population dynamics.Foraging simulation.Swappable grids (Moore / VN / Hex / Dirichlet) 35. Chapter 4: Predator-prey coevolution. 36. Predator scroungingPrey clumping 37. Predator scroungingPrey clumping 38. Predator scroungingPrey clumping 39. Predator characteristicPrey characteristic 40. Predator characteristicPrey characteristic 41. Predator scroungingPrey clumping 42. Predator scroungingPrey clumpingForaging simulation. 43. Predator scroungingGA to optimize predator characteristics (scrounging).Prey clumping 44. Predator scroungingPrey clumpingGA to optimize prey characteristics (clumping) 45. Predator scroungingPrey clumpingGA to optimize predator characteristics (scrounging).GA to optimize prey characteristics (clumping)Foraging simulation. 46. Finis. Questions? 47. Predator scroungingPrey clumping 48. +Before +* x* - 1x+After ++ x* - 1x 49. Relative Payoff Sum Si (t) = xSi (t1) + (1x)ri + Pi (t)where 0 < x < 1 is a memory factor, ri > 0 is the residual value associated with alternative i, Pi (t) is the payo to alternative i at time t, and Si (t) is the value that the animal places on the behavioural alternative i at time t. 50. Relative Payoff Sum Si (t) = xSi (t1) + (1x)ri + Pi (t)where 0 < x < 1 is a memory factor, ri > 0 is the residual value associated with alternative i, Pi (t) is the payo to alternative i at time t, and Si (t) is the value that the animal places on the behavioural alternative i at time t. 51. Relative Payoff Sum Si (t) = xSi (t1) + (1x)ri + Pi (t)where 0 < x < 1 is a memory factor, ri > 0 is the residual value associated with alternative i, Pi (t) is the payo to alternative i at time t, and Si (t) is the value that the animal places on the behavioural alternative i at time t. 52. Relative Payoff Sum Si (t) = xSi (t1) + (1x)ri + Pi (t)where 0 < x < 1 is a memory factor, ri > 0 is the residual value associated with alternative i, Pi (t) is the payo to alternative i at time t, and Si (t) is the value that the animal places on the behavioural alternative i at time t. 53. Relative Payoff Sum Si (t) = xSi (t1) + (1x)ri + Pi (t)where 0 < x < 1 is a memory factor, ri > 0 is the residual value associated with alternative i, Pi (t) is the payo to alternative i at time t, and Si (t) is the value that the animal places on the behavioural alternative i at time t. 54. Perfect Memory Si (t) =+ Ri (t)/( + Ni (t))where Ri (t) is the cumulative payo s from alternative i to time t, Ni (t) is the number of time periods from the beginning in which the option was selected, and are parameters. 55. Perfect Memory Si (t) =+ Ri (t)/( + Ni (t))where Ri (t) is the cumulative payo s from alternative i to time t, Ni (t) is the number of time periods from the beginning in which the option was selected, and are parameters. 56. Perfect Memory Si (t) =+ Ri (t)/( + Ni (t))where Ri (t) is the cumulative payo s from alternative i to time t, Ni (t) is the number of time periods from the beginning in which the option was selected, and are parameters. 57. Perfect Memory Si (t) =+ Ri (t)/( + Ni (t))where Ri (t) is the cumulative payo s from alternative i to time t, Ni (t) is the number of time periods from the beginning in which the option was selected, and are parameters. 58. Linear Operator Si (t) = xSi (t1) + (1x)Pi (t)where 0 < x < 1 is a memory factor, Pi (t) is the payo to alternative i at time t, and Si (t) is the value that the animal places on the behavioural alternative i at time t.