Optimal Bidding Strategies - Essay Example

Running head: Optimal Bidding Strategies Optimal Bidding Strategies Insert Insert Grade Insert 28 February Optimal Bidding Strategies Introduction Optimal bidding strategy is the best decision making criteria used by bidders to maximize their chances of winning and the profitability of winning bids. Independent and uniformly distributed private valuations mean that probability distributions of the bidder’s decisions to bid at a particular price are identically and independently distributed with respect to individual bidder’s private valuation. Private optimal bidding strategies often come out as a result rational decision making based reasonable and sensible thinking with respect to winning and profitability targets of individual bidders. Game theoretical issues in bidding often form the basis of well defined mathematical models used by bidders in arriving at optimal bidding strategies. Bidders often have varying preferences and capabilities, and thus, one bidder’s strategy may directly impact on another bidder’s strategy, although on the basis of private and independent valuations to the bid. The overall market efficiency may also be influential to the impact and effectiveness of optimization strategies that bidders put in place (Eckbo, 2010, P.55-78). First Price Sealed Bid Sealed bids are often rendered as the seller’s monopoly since information regarding the winning bids and their valuations are open to the seller and hidden to buyers as opposed to open forms where information is available to all participants. First price sealed bid auction is basically a bid where each participating bidder submits a sealed bid hidden from other bidding participants to the auctioneer. The first price sealed bid is rather referred as a one shot game since bidders winning chances relies on their one time decision and valuation after which the bid manager opens the bids and determines the highest bid as the winning bid. Bidders with the winning market clearing bids must then pay the amounts they set forward as a one shot bid (McGuigan, Moyer, & Harris, 2011, p.594). The first-price sealed bid auction optimal bidding strategy basically lies on submitting bids below one’s private valuation to maximize surplus. Maximization of a bidders expected surplus is dictated by lower a bid that increases surplus potential although on the other hand it reduces probability a bid becoming successful. More so, the probability of a bid becoming successful increases with valuation increase but decreases with an increase in the number of bidders with regards to their strategies and valuations. The basic optimal strategy for the first-price sealed bid auction is for a bidder to bid below the real valuation in order to make a profit. In case the bidder bids above or equal to the bid valuation, the payment may exceed or equal the bid valuation in case of a win bid, and thus, no optimization is achieved (Sheblé. 1999. P.44-151). There are no interactions among bidders in the first price sealed bid auction, since bids are only submitted by participants once. Participants trade between winning more frequently and maximizing profits, and low bidding with regards to the Nash equilibrium. Optimal bidding strategies under independent and uniformly distributed private valuations among bidders calls for a slight overestimation strategy of the winning bid, considering the fact a bidder has the chance of winning when he or she has the highest estimate irrespective of correct bidding based on averages (Kagel & Levin, 2002, p.2). Assumption that all participating bidders are risk-neutral renders the optimal bidding strategies in the first price sealed bid auction as a bidder that emerges the highest bidder among all bidders bidding the highest expected value (Sheblé, 1999, p.70). The optimal difference between the value that a bidder opts to bid below private valuation and the actual value basically depends on beliefs of the bidder with regards to rival bidder valuation and strategies. This strikes situational interdependence where each bidder varies his or her bidding behavior according to their assessment of valuations of their rivals. However, a bidder may assume their rivals are operating on same strategy as theirs and thereby maximizing expected surplus with respect to the Nash equilibrium. Such assumptions are applicable in case of uniformly distributed private valuations where bidders behave rationally. Optimal bidding strategies in this case preclude bids achieving Pareto efficiency as long as the bidder’s assumptions are correct (Eckbo, 2010, p.32-178). For the first price bid auction, the optimal strategy is basically a trade-off between profitability and the winning probabilities, considering that the bidder’s price higher valuation propensity is the winning probability determinant, while on the other hand, it holds the profitability aspects with respect to lower valuation propensity. Basically, in deciding how much a bidder should bid below the actual perceived value, the bidder should put into consideration the trade-off between reduced winning chances and increased winning profitability with a lower bid. On the other hand, a bidder’s decision on how much to bid above the truth valuation must put into consideration the tradeoff between the increased chances of winning and the reduced profitability of winning with a higher bid price (Anand, Pattanaik, & Puppe, 2009, p.99; Davis & Holt, 1993, p.280). Second Price Sealed Bid The second price sealed bid auction similarly involves submission of sealed bids to the auctioneer by the participant bidders, after which, the highest bidder wins the bid but pays the second highest bid made for the auction (Blaze, 2003, p.57; Dastani, p.152). Dominant strategy in this case is functional under bids equal to private valuation regardless of assumptions to rival bidders. Bidding slightly higher than this private valuation would only render the bid less profitable while bidding lower would imply decreased chances of winning the bid. However, possible bidding manager and bidder collusion may render fair and private valuation unsuccessful where bidders may be influenced towards seek managerial information rather than seeking to have the highest bidding estimates within objective valuations equal to private values. Bidding agent collusion with bidders may not only be illegal, but may also be psychologically compromising to optimal bidding strategies among bidders (Hillier, 1997, p.163). The second price sealed bid auction basically requires honest and truth value bidding as an optimal strategy according to one’s private valuation of the bid, since over estimation would only decrease one’s probability of paying for a higher amount than the actual and reasonable value for the bid in a case of a win. Likewise, bidding below the truth value would lower the bidder’s probability of winning the bid, since lower price would not help in increasing a bidder’s chance of payer for a lower amount considering that the amount to be paid by a winning bidder is the second highest bid price (Schotter, 2009, p.371). However, the true value bidding strategy in second price sealed bid auction may be subjected to auctioneers cheating, where the second highest price to be paid by winning bidder is over-estimated by the auctioneer in an attempt to increase profitability. The true value optimization strategy is thus rendered irrelevant in case of a cheating auctioneer, since no optimization strategy is able to yield the actual winning and profit possibilities (Bidgoli, 2006, p.63). Pareto efficiency in both first price and second price sealed bid auctions refers to the situation where maximization of the sum of utilities of all bidders is achieved with respect to all bidders bidding at their dominant strategies. However, first price sealed bid auction may fail to yield Pareto efficiency under normal circumstances where the highest bid value is not submitted by a bidder whose private valuation is the highest due to risk expectations and preference variations among bidders. It only occurs under conditions that risk expectations and preferences are the same for all participating bidders (Smith, 1991, p.540). Bidding complexities may however render optimal bidding strategies irrelevant incase bidding stages and incentives plans are incorporated with regards to bidding managers putting in place strategies to increase revenue and cost efficiency and effectiveness (Parco &Levy, 2012, p.537). With respect to the second price sealed bid auction, the optimal bidding strategy simply alludes to a true valuation strategy. This is because, in case a bidder bids less than the actual valuation, then the bidders’ chances of winning the bid are significantly reduced. More so, optimal bidding strategies are subject to strategy complexities, since theories place much emphasis on the assumption that all bidders determine their own valuation privately. According to Zhang (2010, p.512), this may include aspects of quantity maximization and impatient bidders who may be regarded to having varying optimization needs and conveniences with regards to best strategies to put forward. Quantity bidders are better of bidding at low prices, while impatient bidders are better off bidding more aggressively as an optimal bidding strategy. However, sealed bid options basically base their functionality on a bidders submitting bids that equal their own valuation with regards to excess value and foregone opportunity possibilities (Eckbo, 2010, p.61). More so, the second price bid auction relies on the competitor’s price for optimization, which thus limits the bidder as a price taker irrespective of the condition under which competitors may subject this influence (Manna, 1997, p.99). Conclusion Optimal Bidding strategies’ outcomes basically have similar targets of maximizing surplus and probability of winning. However, strategy applications vary on the basis of complexities surrounding the bidders as rivals competing for one for a single chance. More so, the bid itself being first price or second price would basically determine what strategies are best for optimization of surplus and winning probabilities although first price and second highest price sealed bids may be determined by more complex aspects such as behavioral and bidding characteristics. Reference List Anand, P., Pattanaik, P., & Puppe, C., 2009. The Handbook of Rational and Social Choice. Oxford: Oxford University Press Inc. Blaze, M., 2003. Financial Crytography: sixth International Conference, FC 2002. Berlin: Springer-Verlag Berlin Heidelberg. Bidgoli, H., 2006. Handbook of Information Security, Volume 3. NJ: John Wiley & Sons. Davis, D.D., & Holt, C.A., 1993. Experimental Economics. Woodstock: Princeton University Press. Dastani, M., 2008. Languages, Methodologies and Development Tools for Multi Agent Systems. Berlin: Springer-Verlag Berlin Heidelberg. Eckbo, B.E., 2010. Bidding Strategies, Financing and Control: Modern Empirical Developments. London: Elsevier Inc. Hillier, B., 1997. The Economics of Asymmetric Information. London: Macmillan Press Ltd. Kagel, J.H., & Levin, D., 2002. Common Value Auctions and the Winner’s Curse. Woodstock: Princeton University Press. Manna, M., 1997. Readings in Microeconomic Theory. London: The Dryden Press, Harcout Brace and Company Ltd. McGuigan, J., Moyer, R.C., & Harris, F.H., 2011. Managerial Economics. OH: Cengage Learning. Parco, J.E., & Levy, D.A., 2012. Attitudes Aren’t Free: Thinking Deeply about Diversity in the US Armed Forces. Enso Books Inc. Sheblé, G.B., 1999. Computational Auction Mechanism for Restructured Power Industry Operation. MA: Kluwer Academic Publishers. Schotter, A., 2009. Microeconomics: A Modern Approach. OH: Cengage Learning, Inc. Smith, V.L., 1991. Papers in Experimental Economics. Cambridge University Press. Zhang, B., 2010. Trends in Artificial Intelligence. Berlin: Springer-Verlag Berlin Heidelberg. Read More