1. On the value of efficient search: the case of biological prospecting Christopher Costello* and Michael Ward CREE 2003, Victoria B.C. * Bren School of Environmental Science & Management, UC Santa Barbara

2. Bioprospecting, search, and biodiversity conservation Are the private sector incentives sufficient to protect biodiversity? Bioprospecting: The search for valuable biological products in nature. Private sector incentive to protect places in which future cures may be found. –Requires model of biodiversity, costly search, uncertain discovery.

3. The literature Early literature: No search, no redundancy, no search cost. High values ($24 million per untested species) Simpson et al. (JPE 1996): Bioprospecting incentives vanishingly small ($21/ha) –Whether p is high or low Rausser and Small (JPE 2000): Information rents dramatically increase marginal values ($9,177/ha).

4. Ordered search in bioprospecting Same data set is used –18 biodiversity hotspots, range of density of endemic species, range of sizes of hotspots. Information allows “leads” to be ordered in most efficient manner. –Generates information rents for the best leads. Suggests that ordered search may add significantly to value to the collection.

5. Modeling the value of a collection of leads –N research leads: 1, 2,…, n,…, N –Success probabilities: p 1, p 2, …, p N –Search cost: c –Revenue upon success: R Value of a collection (S):

6. The value of information “Organizing scientific framework” may allow leads to be searched in a more efficient order. In expectation, queuing efficiency saves search costs – this is source of value. Value of moving from queue S to queue S*:

7. An experiment Use bioprospecting data set. Compare marginal values and value of collection under: 1.Optimally ordered search (expect $9,177/ha) 2.Random search (expect $21/ha) 3.Backwards search (expect < $21/ha)

8. Results of experiment Search efficiency is empirically unimportant using bioprospecting data (VOI=$1M=2%). HotspotM. Value $ (optimal) M. Value $ (random) M. Value $ (backwards) W. Ecuador9,1778,8368,455 Sri Lanka7,4637,1906,882 New Caledonia5,4735,2775,056 Collection42.2 x 10 6 41.2 x 10 6 40.1 x 10 6

9. When is information valuable? Comparative statics on  (S*,S) reveal conditions under which search efficiency adds value. 1.Independent of success payoff, R 2.Linearly increasing in search cost, c 3.Increasing in queue length (add random lead) 4.Decreasing in , for p i +  5.Increasing in spread, holding overall success probability constant.

10. Revisit bioprospecting example Is low VOI due to features of the bioprospecting problem? –Large # leads (74,600) –Low probabilities of success (1 in 100,000) –High spread of probabilities (2 orders of magnitude) These all act to increase  (S*,S). Other problems (fewer leads, smaller search cost, higher probs, less spread) would have even smaller  (S*,S).

11. VOI response to parameters Individual parameter changes: (base VOI=$1M) –$1 decrease in c: $2,000 decrease in  (S*,S). –Eliminate 1 random lead: $23 decrease in  (S*,S). –Increase each p i by.000012: $300,000 decrease in  (S*,S). –Decrease p max by 1% increase p min : $100,000 decrease in  (S*,S). Combined changes (c in half, N in half, increase p i by.000012, decrease spread by half): VOI = $52,000.

12. Implications and generalizations Using BP data efficient search has low value. But, efficient search can have high value: –E.g. Many leads, 1 sure thing, many low quality, so information can save nearly all search cost. Information on lead success may have value for other reasons: 1.Truncate long lists of leads 2.Prevent destruction of especially promising leads

13. Truncating long lists of leads If queue contains enough poor quality leads, may discourage (random) search entirely. Consider information to sort list into two piles: (1) worthy of search, (2) discards. –Those worthy of search (p>c/R) are searched randomly –Discards (p<c/R) are removed from queue. What is the value of this kind of information? How does this value compare with value of efficiently queuing a given list?

14. Value of a lead vs. value of efficient queuing Value of a lead Value of optimally ordering the lead Lead Value: 93%: add to queue 7%: order efficiently

15. Conservation implications of information Conversion of habitat -- loss of species. Suppose hotspots lost if no protection – which sites to save? –Answer hinges critically on information Full info: save the best sites No info: all sites have average value, don’t know which to save. –With information an option value obtains After p i is learned, can decide on best course of action (preserve the site or allow it to be developed).

16. Threat and option value How much would firm pay to learn p i ? –No Threat: Allows better ordering: $19. –Threat: must pay  to save the lead 1.Learn p i 2.Decide whether worth saving (pay  ) or not. 3.If save, order appropriately and search. If  = $500: OV = $232. If  = $1,000: OV = $133. Information can have significant value when sites are threatened with irreversible conversion.

17. Conclusions BP literature: large bang from efficient search Our experiment: efficient search is empirically unimportant (only 2% gain over random search) Showed how model features affect VOI –Features of BP problem make VOI high –Other search problems have even lower VOI. Information can have value for other reasons –Truncate long lists of leads –Conserve the most promising habitats when threatened with irreversible conversion (option value obtains).