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Modeling Fishermen Decision-Making

Modeling Fishermen Decision-Making. ~Presentation by John Lynham to the F-cubed group, May 26 th ,2005. Two Projects. Empirical Estimation of probability of fishing in a particular location  simulation Testing different theories of information processing and learning by fishermen.

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Modeling Fishermen Decision-Making

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  1. Modeling Fishermen Decision-Making ~Presentation by John Lynham to the F-cubed group, May 26th,2005

  2. Two Projects Empirical Estimation of probability of fishing in a particular location  simulation Testing different theories of information processing and learning by fishermen

  3. Empirical Estimation • The standard statistical tool used in estimating the probability of making a choice is a multinomial or conditional logit model. • For fisheries, the Repeated Nested Logit (RNL) model developed by McFadden (1978) is often used

  4. go fishing Don’t go fishing Then choose where… Repeated Nested Logit

  5. Smith and Wilen (2003)“Economic impacts of marine reserves: the importance of spatial behavior”, JEEM

  6. Smith and Wilen (2003)“Economic impacts of marine reserves: the importance of spatial behavior”, JEEM Fishermen decision-making depends on: • Wave period • Wind speed • Wave height • Day of the week • Distance to the location • Expected Revenue In their model, fecundity is size-specific, growth is patch-specific, mortality is Beverton-Holt if above the size-limit… They conclude: “optimistic conclusions about reserves may be an artifact of simplifying assumptions that ignore economic behavior”p. 183

  7. Simulations with stochasticity • Is their model missing one of the important benefits of reserves: “ecological lenders of last resort” during negative shocks to the system? • Would their simulations change in a meaningful way if stochastic shocks were introduced?

  8. Fleet Modeling “We also thought about the feasibility of adding in some stochasticity.  We abandoned the idea with our model because of the dimensionality of it (360 age classes, 11 areas, 4 ports with different economic models, all simulated over a 1200 months).  We concluded the model size as it is now configured would make running multiple runs with different shocks and different shock seeds a computational nightmare.”   -Jim Wilen  need for a simpler, fleet-based model.

  9. Information Search and Social Learning 1. Modeling Spatial Decision-Making as a Multi-Armed Bandit problem 2. Modeling Informational Cascades and Peer Effects

  10. The Bandit Problem • A room filled with n slot machines… • “Search and learning with correlated information: Theory and evidence from professional fishermen” - Phillipe Marcoul and Quinn Weninger • Fishermen are not myopic but “follow rational search and learning strategies.” • “Our results suggest that development of tractable econometric methodologies consistent with rational search and learning should improve the understanding of the spatial movements of fishermen and may improve fisheries management.”

  11. Informational Cascades and Peer Effects • The Discipline of Market Leaders • “rational herding” • “social learning” • “informational cascades”

  12. Growing body of research • Anderson and Holt (1997). • Chaudhuri, Chang and Jayaratne (1997).   • Klein and Majewski, 1996. • Welch (1992). • Iowa and New Hampshire primaries…

  13. Relevant Empirical Research • Conley and Udry, 2001, “Social Learning through networks: The Adoption of New Agricultural Technologies in Ghana,” AJAA, • Duflo and Saez, 2002. “Participation and Investment Decisions in a Retirement Plan: the influence of Colleagues’ choices.” Journal of Public Economics. • A common roadblock is endogeneity…

  14. A very simple model • The payoff to fishing, F, is either 1 (there’s lots of fish) or –1 (there’s no fish and I’ve wasted my time searching for them) • Each individual receives private information about the stock of fish at the location, Good or Bad. What are the networks? • Specifically, each individual observes Good with probability p=.75 if F=1 and with probability .25 if F=-1.

  15. An example… Adam goes fishing if his private information is Good and doesn’t if it is Bad. All subsequent fishermen can infer Adam’s private information perfectly from his decision. The second individual, Barbara, observes person Adam’s action. If Adam went fishing and Barbara’s private information is Good than Barbara infers that there are two people with information that says the fishing is good at the location. Barbara decides to go fishing.

  16. An example… Now suppose Chris comes along, sees Adam and Barbara out in their boats but receives a private signal that the fishing is bad. If Chris took into account just Adam’s Good signal and his own Bad signal then he believes that the value to fishing is equally likely to be –1 or 1. But Chris also knows that Barbara is more likely to have seen a Good signal than a Bad signal and this tips the balance in favor of going fishing. The same thing happens with Deirdre and so on…

  17. Research Goals 1. Simplify Smith and Wilen (2003) into a fleet model and introduce stochastic shocks and uncertainty 2. Test a variety of information, learning and social network theories using fishermen decision-making data 3. If I find that any of these theories stick, then this changes how I should approach the fleet modeling…

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