Online Bidding Behaviour And Loss Aversion In Cloud Computing Markets: An Experiment

The last few years have witnessed a rapid growth in commoditization and consumption of IT services particularly due to the growing acceptance of cloud computing services. This in turn has led to newer forms of pricing the cloud services such as dynamic pricing. Infact, spot pricing, a dynamic pricing scheme has become mainstream. Cloud consumers using these schemes need to place their bids inorder to procure computing instances. Most of extant research on cloud dynamic pricing focuses on resource allocation problems and bidding strategies. We identify the need to look at behavioural biases of bidders to bring in a holistic perspective to cloud dynamic pricing discussions. In this paper, we conduct an experiment to elicit the impact of a behavioural bias namely, loss aversion, on a cloud consumer’s bidding behaviour. We discuss the social implications of our result to cloud consumers and the economic implications for cloud providers.

[1]  Marc Premm,et al.  Bid price control and dynamic pricing in clouds , 2009, ECIS.

[2]  Daniel Kahneman,et al.  Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias , 1991 .

[3]  F. Strack,et al.  Explaining the Enigmatic Anchoring Effect: Mechanisms of Selective Accessibility , 1997 .

[4]  Jens-Martin Bramsen.  A pseudo-endowment effect in internet auctions , 2008 .

[5]  Yong Meng Teo,et al.  Dynamic Resource Pricing on Federated Clouds , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[6]  Artur Andrzejak,et al.  Decision Model for Cloud Computing under SLA Constraints , 2010, 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.

[7]  Gillian Ku,et al.  Auctions and Auction Fever: Explanations from Competitive Arousal and Framing , 2001 .

[8]  Eric J. Johnson,et al.  Individual-level loss aversion in riskless and risky choices , 2022, Theory and Decision.

[9]  D. Kliger,et al.  Evaluation periods and asset prices: Myopic loss aversion at the financial marketplace , 2009 .

[10]  D. Ariely,et al.  Buying, Bidding, Playing, or Competing? Value Assessment and Decision Dynamics in Online Auctions , 2003 .

[11]  D. Kahneman,et al.  Experimental Tests of the Endowment Effect and the Coase Theorem , 1990, Journal of Political Economy.

[12]  W. Güth,et al.  Loss Aversion and Learning to Bid , 2009 .

[13]  David C. Parkes,et al.  Rationality and Self-Interest in Peer to Peer Networks , 2003, IPTPS.

[14]  A. Tversky,et al.  Loss Aversion in Riskless Choice: A Reference-Dependent Model , 1991 .

[15]  Subhajyoti Bandyopadhyay,et al.  Cloud Computing - The Business Perspective , 2011, 2011 44th Hawaii International Conference on System Sciences.

[16]  T. Schelling.  Micromotives and Macrobehavior , 1978 .

[17]  Cary Deck,et al.  Personality and the Consistency of Risk Taking Behavior: Experimental Evidence , 2010 .

[18]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[19]  Christof Weinhardt,et al.  Business Models in the Service World , 2009, IT Professional.

[20]  Jeff T. Larsen,et al.  Comparing Gains and Losses , 2010, Psychological science.

[21]  J. Kuruzovich.  Time and Online Auctions , 2012 .

[22]  Muli Ben-Yehuda,et al.  Deconstructing Amazon EC2 Spot Instance Pricing , 2011, CloudCom.

[23]  N. Liberman,et al.  Predicting the intensity of losses vs. non-gains and non-losses vs. gains in judging fairness and value: A test of the loss aversion explanation , 2005 .

[24]  A. Tversky,et al.  Judgment under Uncertainty , 1982 .

[25]  K. McConnell,et al.  A Review of Wta/Wtp Studies , 2000 .

[26]  Werner Reinartz.  Customizing Prices in Online Markets , 2002 .

[27]  Sewook Wee,et al.  Debunking Real-Time Pricing in Cloud Computing , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[28]  Rajkumar Buyya,et al.  Statistical Modeling of Spot Instance Prices in Public Cloud Environments , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[29]  H. Bleichrodt,et al.  A tractable method to measure utility and loss aversion under prospect theory , 2008 .

[30]  T. Schelling,et al.  Micromotives and Macrobehaviour. , 1980 .

[31]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[32]  Noam Nisan,et al.  The POPCORN market. Online markets for computational resources , 2000, Decis. Support Syst..

[33]  Joe Weinman,et al.  The future of Cloud Computing , 2011, 2011 IEEE Technology Time Machine Symposium on Technologies Beyond 2020.

[34]  Prashant Pandey,et al.  Cloud computing , 2010, ICWET.

[35]  J. Knetsch,et al.  Willingness to Pay and Compensation Demanded: Experimental Evidence of an Unexpected Disparity in Measures of Value , 1984 .

[36]  D. Kahneman,et al.  Timid choices and bold forecasts: a cognitive perspective on risk taking , 1993 .

[37]  K. Sowmya,et al.  Generalized second price auction for relatively ranked heterogeneous spot instances in the cloud , 2013, 2013 Fifth International Conference on Advanced Computing (ICoAC).

[38]  Xuanming Su.  A Model of Consumer Inertia With Applications to Dynamic Pricing , 2008 .

[39]  U. Dholakia,et al.  Coveted or Overlooked? The Psychology of Bidding for Comparable Listings in Digital Auctions , 2001 .

[40]  William D. Schulze,et al.  The Disparity Between Willingness to Accept and Willingness to Pay Measures of Value , 1987 .

[41]  Jon Kristian Heimdal.  Myopic Loss Aversion and the Equity Premium Puzzle , 2013 .

[42]  Ayse Öncüler,et al.  Effects of study design characteristics on the WTA–WTP disparity: A meta analytical framework , 2005 .

[43]  Katherine L. Milkman,et al.  Policy Bundling to Overcome Loss Aversion: A Method for Improving Legislative Outcomes , 2009 .

[44]  Richard H. Thaler,et al.  Mental Accounting and Consumer Choice , 1985, Mark. Sci..