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Information Sharing and Cooperative Search in Fisheries


We present a dynamic game of search and learning about the productivity of competing fishing locations. Perfect Bayesian Nash equilibrium search patterns for non-cooperating fishermen and members of an information sharing cooperative are compared with first-best outcomes. Independent fishermen do not internalize the full value of information, and do not replicate first-best search. A fishing cooperative faces a free-riding problem, as each coop member prefers that other members undertake costly search for information. Pooling contracts among coop members may mitigate, but are not likely to eliminate free-riding. Our results explain the paucity of information sharing in fisheries and suggest regulators use caution in advocating cooperatives as a solution to common pool inefficiencies in fisheries.

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  1. According to U.S. Department of Agriculture (2012), there were 37 fishing cooperatives operating in the US in 2010. See Kitts and Edwards (2003) for a discussion of US legislation, the Fisheries Collective Marketing Act of 1934 and the American Fisheries Act of 1998, that allow the formation of fishing cooperatives.

  2. Management problems ranging from bycatch, to the design of marine protected areas have been linked to problems of information acquisition and information sharing in fisheries (Abbott and Wilen 2010; Marcoul and Weninger 2008; Costello and Deacon 2007; Curtis and McConnell 2004). The North Pacific Fishery Management Council (2011) discusses the use of fishing cooperatives in the Gulf of Alaska Pollock fishery to reduce incidental catch of Chinook salmon, stating cooperatives will “facilitate information sharing and fleet coordination that could be important to achieving Chinook avoidance (pp. 3, emphasis added).” Amendment 16 to the Northeast Multispecies Fisheries Management Plan pushes for expansion of the use of fishing cooperatives (New England Fishery Management Council 2010).

  3. Palmer (1990) finds that radio transmissions of Maine lobster fishermen convey detailed information about lobster size and fishing locations during pre-molting periods when lobster are hidden in rocks and less accessible to trap gear, i.e., information is shared when it has little value.

  4. Gatewood (1984) identifies an example of such a synergy in the Alaskan purse seine fishery, where managers tightly control the length of openings. The author notes that “While it is true that one boat can scout as wide an area in four days as four boats can in one day, the utility of the information collected by the four boats scouting the day before the opening is much greater, provided they share what each has observed (pp. 362).” It is important to note that the information gathering setting for salmon seiner’s differs from the one that is modeled in this paper.

  5. Halibut are bottom fish that are not observable with sonar equipment; fishing success is not known until gear is soaked and retrieved and captured fish are counted.

  6. The assumption that \(\varepsilon _{i,j}\) follows a known distribution simplifies the description of learning in the model, and is a standard component of Bayesian learning models (e.g., Marcoul and Weninger 2008; Mangel and Clark 1986).

  7. The model can be extended to consider correlation in beliefs (Marcoul and Weninger 2008). Correlated beliefs alter search behavior quantitatively. Our main insights regarding the efficiency of independent search and free-riding within fishing coops do not change under correlated beliefs.

  8. Adding a third choice, “stay at port,” complicates notation with few additional insights for information sharing.

  9. Alternate specifications for the congestion penalty were explored, e.g., site specific congestion penalties whose size was a function of the observed payoff signal (\(\kappa _j=\kappa (s_{i,j})\)). We found no qualitative change in the results and therefore maintain the simpler formulation.

  10. The common prior is a standard assumption in the literature and a good starting point for our analysis. For a thorough description of the literature on the common prior assumption see Morris (1995).

  11. Though analytic solutions are preferred, two of the first-best expected payoff functions, \(W_{1,2}\) and \(W_{2,1}\), have no closed form solution. As such, we turn to numeric methods to analyze equilibrium search patterns and provide comparative dynamics on model parameters.

  12. Our approach follows the “new institutional economics” viewpoint, which emphasizes the role of transactions costs, property rights, and agency relationships for understanding organizational structure (Fama 1980; Williamson 1975; Alchian and Demsetz 1972). A comprehensive review of contracts that assign rights and residual claims within a fishing cooperative is beyond the scope of this paper. See Fama and Jensen (1983) for additional discussion of contracting in producer organizations.

  13. The pooling of profit, revenue, and/or cost is common among existing fishing cooperatives; see Uchida and Baba (2008), Knapp (2008), Gaspart and Seki (2003) for examples.

  14. The shape and location of \(\bar{V}^c\) and \(V_i\) will also depend on beliefs. Our numerical simulations reveal that \(V_i\) can exceed \(\bar{V}^c\) over some regions of the belief space. This result arises because information, which is more available in a coop, lowers belief variance, which implies lower probability of extreme high payoff outcomes.


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Correspondence to Keith S. Evans.

Additional information

The authors thank Charles Zheng, Michael Springborn, and seminar participants at the North American Association of Fisheries Economists Forum 2009, Applied Agriculture and Economics Association Meeting 2009, and Heartland Environmental and Resource Economics Workshop 2009 for helpful suggestions. Weninger acknowledges financial support from the National Science Foundation under grant SES # 0527728.

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Appendix 1: Belief Updating

Three belief updating scenarios are possible in this model: no signal, one signal, or two signals about the profitability of a fishing site. Under the independence assumption—payoffs across sites are independent—a signal from site \(k\) provides no new information about site \(j\). As such, updated beliefs at site \(j\) equal the prior belief, regardless of the information observed in the signal \(s_{i,k}\).

Suppose, instead, that a single signal from site \(j\) is observed. For prior beliefs \(\mu _{i,j},\ \sigma ^2_{i,j}\), and single payoff signal \(s_{i,j}\), the updated beliefs of fisherman \(i\) at site \(j\) are given as,

$$\begin{aligned} \mu '_{i,j}\left| s_{i,j}\right.&= \theta _{i,j}\mu _{i,j} + (1-\theta _{i,j})s_{i,j} \\ \sigma ^{2'}_{i,j}\left| s_{i,j}\right.&= \theta _{i,j} \sigma ^2_{i,j}, \end{aligned}$$

where \(\theta _{i,j}=\sigma ^2/(\sigma ^2+\sigma ^2_{i,j})\in [0,1]\) weights the new information contained in the signal against prior beliefs. As uncertainty is resolved, more weight is placed on the prior.

Suppose now that two signals are available to fisherman \(i\) from site \(j\); one from fisherman \(i\) and the other from \(-i\). Then updated beliefs at \(j\) are given as,

$$\begin{aligned} \mu '_{i,j}\left| s_{i,j},s_{-i,j}\right.&= \theta _{i,j}~\mu _{i,j} + (1-\theta _{i,j})~\bar{s}_j \\ \sigma ^{2'}_{i,j}\left| s_{i,j},s_{-i,j}\right.&= \theta _{i,j}\sigma ^2_{i,j}, \end{aligned}$$

where \(\bar{s}_j\) denotes the average payoff signal observed at site \(j\) and \(\theta _{i,j}=\sigma ^2/(\sigma ^2+2\sigma ^2_{i,j})\in [0,1]\) again weights the new information (from two signals) against prior beliefs.

Appendix 2: Bayesian Nash Equilibrium

This section solves the date \(t_2\) Bayesian game for independent risk neutral fishermen. We solve for the thresholds \(s_i^U, s_i^L, s_{-i}^U\), and \(s_{-i}^L\) from (4), which separate feasible signals into regions. There are four possible scenarios to consider depending on the period 1 site choices. We present the calculations for the case where both fishermen fish site 1 in the first fishing period. The analysis of the remaining cases follows analogously and is not repeated.

We begin by specifying how fishermen form beliefs about beliefs. Since both fishermen fished site 1 in the first period, \(i\)’s belief about \(-i\)’s private information should be a function of \(s_{i,1}\), i.e., \(i\)’s private information obtained from fishing site 1. The best information available about \(-i\)’s beliefs comes from \(i\)’s updated beliefs. As such, each fisherman believes they share the same updated beliefs about sites 1 and 2.

Suppose \(s_{-i}^U\) and \(s_{-i}^L\) exist such that the representative fisherman’s counterpart plays the strategy outlined in Eq. (4). For the representative fishermen with \(s_{i,1} \in [s_i^L,\ s_i^U]\) to play a mixed strategy at \(t_2, a'_i \in (0,1)\), it must be the case that, based on his conjecture about his counterpart’s strategy, he is indifferent between fishing site 1 and site 2. Setting the expected payoff of fishing these sites equal and solving for \(a'_{-i}\) finds,

$$\begin{aligned} a'_{-i} = \frac{\mu '_{i,1} - \mu '_{i,2} - \kappa + 2\kappa \Pr \left( s_{-i,1} < s_{-i}^U|s_{i,1}\right) }{2\kappa \Pr \left( s_{-i}^L \le s_{-i,1} \le s_{-i}^U | s_{i,1}\right) } \quad \forall \ s_{i,1} \in \left[ s^L_i,\ s^U_i\right] . \end{aligned}$$

Using the same method, we can calculate the mixed fishing strategy for the representative fisherman, \(a'_i\).

In a BNE, each player’s action must be optimal subject to their Bayesian updated belief about the strategy of their rival. Using \(a'_i\) and \(a'_{-i}\), we derive four equations that define the parameters \(s_i^U, s_i^L, s_{-i}^U\), and \(s_{-i}^L\) that will satisfy a BNE.

The representative fisherman believes that his counterpart is indifferent between mixing and fishing site 1 when \(a'_{-i}\) exactly equals one (his counterpart knows that this is the representative fisherman’s belief). For site 1 to be strictly dominant, it must be that site 1 is still preferred even under the worst case (when both fish the site). Using this fact, the updating rules in Appendix 1, and the strategy profile in Eq. (4), the representative fisherman’s upper signal threshold \(s_i^U\) must satisfy

$$\begin{aligned} s_i^U = \frac{1}{1-\theta _{i,1}}\left\{ \mu _{i,2} - \theta _{i,1}\mu _{i,1} +\kappa \right\} \end{aligned}$$

where \(\theta _{i,1}=\sigma ^2/(\sigma ^2+\sigma ^2_{i,1})\).

Similarly, fisherman \(i\) believes his counterpart is indifferent between mixing and fishing site 2 when \(a_{-i}\) exactly equals zero (his counterpart knows that this is the representative fisherman’s belief). For site 2 to be strictly dominant, it must be that site 2 is still preferred even under the worst case (when both fish the site). Using this fact and given the strategy profile, the representative fisherman’s lower signal threshold \(s_i^L\) must satisfy

$$\begin{aligned} s_i^L = \frac{1}{1-\theta _{i,1}}\left\{ \mu _{i,2}-\theta _{i,1}\mu _{i,1} -\kappa \right\} . \end{aligned}$$

Using the same argument, we construct the following for fisherman \(-i\):

$$\begin{aligned} s_{-i}^U = \frac{1}{1-\theta _{-i,1}}\biggl \{\mu _{-i,2} - \theta _{-i,1}\mu _{-i,1} +\kappa \biggr \} \end{aligned}$$
$$\begin{aligned} s_{-i}^L = \frac{1}{1-\theta _{-i,1}}\biggl \{\mu _{-i,2} - \theta _{-i,1}\mu _{-i,1} -\kappa \biggr \} . \end{aligned}$$

The thresholds that satisfy the BNE , \(s_i^U, s_i^L, s_{-i}^U\), and \(s_{-i}^L\), defined by Eqs. (12)–(15), must simultaneously hold.

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Evans, K.S., Weninger, Q. Information Sharing and Cooperative Search in Fisheries. Environ Resource Econ 58, 353–372 (2014).

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  • Search
  • Information sharing
  • Bayesian learning
  • Fisheries cooperatives

JEL Classification

  • Q22
  • D8