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Evaluating an agent based event driven foraging model using experimental data

Evaluating an agent based event driven foraging model using experimental data. Abran Steele-Feldman University of Washington. Outline. Overview of the event driven approach The experiment The model for the experiment Analyzing the model Testing the model. Basic Framework.

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Evaluating an agent based event driven foraging model using experimental data

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  1. Evaluating an agent based event driven foraging model using experimental data Abran Steele-Feldman University of Washington

  2. Outline • Overview of the event driven approach • The experiment • The model for the experiment • Analyzing the model • Testing the model

  3. Basic Framework • Behaviors: the actions available to the organism. • Utility: organisms choose behaviors to maximize rewards. • Not omniscient, utility not constant. Expected utility: based on organisms’ experience and current state.

  4. Events Expected Utility Behaviors Chosen • How does experience affect the evaluation of expected utility? • Through Events. • Discrete, binary, thresholds • The pattern of events in time determines the sequence of behavior.

  5. U(t) = expected utility • u = intrinsic utility • c = energetic cost • m = memory coefficient • 0<= m <= 1 • e(t) = event General Equations e = 0 e = 1

  6. The Experiment ? • Food pellets delivered stochastically at different rates- good side/bad side. • Data: pellet release times and each time a fish enters or leaves a patch. • Pellet encounters? Must assume.

  7. Experimental Details • Wildhaber and Crowder conducted a series of 4 experiments with different pellet delivery rates. • Eight tanks. 14 days. • Changeover on 8th day. • 2 hour feeding period. Data for entire day. • Giving up time: the time a fish remains in a patch after the last event

  8. The Model for the experiment • Three behavior streams: • BL: Local Foraging- stay here and wait for food • BGA: Global Foraging- go to side A • BGB: Global Foraging- go to side B • Only two possible behaviors at any given time: can’t go to side A if already on side A. • Two types of events: • e1S: food pellet seen/eaten on side S • e2S: arriving on patch S

  9. where t’ is the time of arrival on patch S • u = intrinsic utility • m = memory coefficient, 0<= m <= 1 • mL< mG

  10. Time Fish Leaves - Time Last Event = Giving Up Time

  11. where t0 is the time of the last event Analyzing the Model • Derived giving up time:

  12. Can predict giving up time for each patch visit based on a fish’s pellet experience and time of arrival. • 5 parameters to set: • The memory coefficients mL and mG • The initial global probabilities pGA(0) and pGB(0) • The intrinsic utility uG

  13. Testing the model • Aggregate data vs. individual data • The changeover day: of key importance? • Trends Reverse • Initial test: calibrate the model by hand on data from day 7, then predict the giving up times for day 8.

  14. Day 8 Correlation Coefficient: .623 Day 7 Correlation Coefficient: .724 • mL = .96 mG= .9992 pGA(0)= .002pGB(0)=.00008 uG = 7

  15. Where do we go from here? • Striking Initial Results • Formal test: • Use POMAC (Pareto Optimal Model Assessment Cycle) to calibrate. • Calibrate on first week of data. • Test on the second week of data. • Is this a reasonable test?

  16. Areas to explore • Incorporating new behaviors or events. • Optimality • Outside the lab?

  17. Acknowledgements • Jim Anderson • Mark Wildhaber • QERM

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