The optimizer’s curse phenomenon seen in portfolio optimization appears similar to the winner’s curse observed in competitive bidding. A technique that calculates the effects of optimizer’s curse and sampling error bias is adapted for analyzing competitive bidding. The demonstration example is an auction of single risky project, which could be an exploration block containing one prospect. Bid optimization involves finding a function that expresses bid fraction as a function of estimates for 1) project expected monetary value and 2) number of other bidders. A foundation principle for a symmetric auction is that each bidder must be in equilibrium. The base model assumes that all auction competitors a) have matching judgments for the project parent population (in three parameters), b) all competitors the same estimate/actual error functions (in the three judgment parameters), c) use the same bid factor function, and 4) are risk neutral. Several correlations are embedded in the calculations. Incorporating specific company cost and information advantages, sub-models representing the project appraisal process to determine commercial success, and other such details is straightforward. The example assumes one company (us) has assessed chance of success, test cost, and discovery value for a subject prospect. A brute-force Monte Carlo simulation approach solves Bayesian calculations for the auction simulation. The calculations produce estimates of 1) probability of winning the auction and 2) expected monetary value versus bid amount, thus determining the optimal bid.

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