The Role of Critical Mass in Establishing a Successful Network Market: An Experimental Investigation

The Role of Critical Mass in Establishing a Successful Network Market: An Experimental Investigation
Date: 2015-05-12
By: Bradley J. Ruffle, Avi Weiss, Amir Etziony (Wilfrid Laurier University)
URL: http://d.repec.org/n?u=RePEc:wlu:lcerpa:0092&r=net
A network market is a market in which the benefit each consumer derives from a good is an increasing function of the number of consumers who own the same or similar goods. A major obstacle that plagues the introduction of a network good is the ability to reach critical mass, namely, the minimum number of buyers required to render purchase worthwhile. This can be likened to a coordination game with multiple Pareto-ranked equilibria. Through a series of experiments, we study consumers’ ability to coordinate on purchasing the network good. Our results highlight the central importance of the level of the critical mass. Neither an improved reward-risk ratio through lower prices nor previous success at a lower critical mass facilitates the establishment of a network market when the critical mass is sufficiently high.
Keywords: experimental economics, network goods, coordination game, critical mass
JEL: C92 L19

Sequential selling and information dissemination in the presence of network effects

Sequential selling and information dissemination in the presence of network effects

Date:	2014-10
By:	Junjie Zhou (School of International Business Administration, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai, 200433, China) Ying-Ju Chen (School of Business and Management & School of Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)
URL:	http://d.repec.org/n?u=RePEc:net:wpaper:1404&r=net
	In this paper, we examine how a seller sells a product/service with a positive consumption externality, and customers are uncertain about the product’s/service’s value. Because early adopters learn this value, we consider the customers’ intrinsic signaling incentives and positive feedback effects. Anticipating this, the seller commits to provide price discounts to the followers, and charges the leader a high price. Thus, the profit-maximizing pricing features the cream skimming strategy. We also show that the lack of seller’s commitment is detrimental to the social welfare; nonetheless, the sequential selling still boosts up the seller’s profit. Embedding a physical network with arbitrary payoff externality among customers, we investigate the optimal targeting strategy in the presence of information asymmetry. We provide precise indices for this leader selection problem. For undirected graphs, we should simply choose the player with the highest degree, irrespective of the seller’s commitment power. Going beyond this family of networks, in general the seller’s commitment power affects the optimal targeting strategy.
Keywords:	revenue management; signaling; information transmission; social networks;
JEL:	D82 L14 L15

Groups Make Better Self-Interested Decisions

Gary Charness and Matthias Sutter (2012) “Groups Make Better Self-Interested Decisions." Journal of Economic Perspectives, 26(3): 157-76. DOI: 10.1257/jep.26.3.157; URL: AEAWeb. PDF1; PDF2

==Notes by yinung==

集體決策和個人決策之差異:

從賽局理論的角度看, 集體決策較個人決策來得理性 (rational), 因其可避免認知錯誤與限制。

不過集體決策也因此不見得會提高社會福利 (集體決策比較 self-interest)

待研究問題:

the optimal size of the group ( A useful starting point here is Forsyth’s (2006) work)

==original Abstract==

In this paper, we describe what economists have learned about differences between group and individual decision-making. This literature is still young, and in this paper, we will mostly draw on experimental work (mainly in the laboratory) that has compared individual decision-making to group decision-making, and to individual decision-making in situations with salient group membership. The bottom line emerging from economic research on group decision-making is that groups are more likely to make choices that follow standard game-theoretic predictions, while individuals are more likely to be influenced by biases, cognitive limitations, and social considerations. In this sense, groups are generally less “behavioral" than individuals. An immediate implication of this result is that individual decisions in isolation cannot necessarily be assumed to be good predictors of the decisions made by groups. More broadly, the evidence casts doubts on traditional approaches that model economic behavior as if individuals were making decisions in isolation.

==References==

• Forsyth, Donelson R. 2006. Group Dynamics, 4th edition. Belmont, CA: Thomson Higher Education.
• Camerer, Colin F.  2003.  Behavioural Game Theory: Experiments in Strategic Interaction. Princeton University Press.
• Charness, Gary, Edi Karni, and Dan Levin. 2007. “Individual and Group Decision Making under Risk: An Experimental Study of Bayesian Updating and Violations of First-Order Stochastic Dominance.” Journal of Risk and Uncertainty 35(2): 129–48.
• Charness, Gary, Edi Karni, and Dan Levin. 2010. “On the Conjunction Fallacy in Probability Judgment: New Experimental Evidence Regarding Linda.” Games and Economic Behavior 68(2): 551–56.
• Charness, Gary, and Dan Levin. 2005. “When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect.” American Economic Review 95(4): 1300–1309.
• Charness, Gary, Luca Rigotti, and Aldo  Rustichini. 2007. “Individual Behavior and Group Membership.”  American Economic Review 97(4):  1340–52.
• Chen, Yan, and Xin Li. 2009. “Group Identity and Social Preferences.” American Economic Review 99(1): 431–57.
• Cooper, David J., and John H. Kagel. 2005. “Are Two Heads Better Than One? Team versus Individual Play in Signaling Games.” American Economic Review 95(3): 477–509.
• Fahr, René, and Bernd Irlenbusch. 2011. “Who Follows the Crowd—Groups or Individuals?” Journal of Economic Behavior and Organization 80(2): 200–209.
• Feri, Francesco, Bernd Irlenbusch, and Matthias Sutter.  2010. “Efi  ciency  Gains  from Team-Based Coordination—Large-Scale Experimental Evidence.”  American Economic Review 100(4): 1892–1912.
• Holmstrom, Bengt.  1982. “Moral Hazard in Teams.” Bell Journal of Economics 13(2): 324–40.
• Kocher, Martin G., Sabine Strauss, and Matthias Sutter.  2006. “Individual or Team Decision-Making— Causes and Consequences of Self-Selection.” Games and Economic Behavior 56(2): 259–70.
• Kocher, Martin G., and Matthias Sutter.  2005. “The Decision Maker Matters: Individual versus Group Behavior in Experimental Beauty-Contest Games.” Economic Journal 115(500): 200–223.
• Kugler, Tamar, Gary Bornstein, Martin G. Kocher, and Matthias Sutter. 2007. “Trust between Individuals and Groups: Groups are Less Trusting Than Individuals But Just as Trustworthy.” Journal of Economic Psychology 28(6): 646–57.
• Laughlin, Patrick R., Bryan L. Bonner, and Andrew G. Miner. 2002. “Groups Perform Better Than the Best Individuals on Letter-to-Numbers Problems.”  Organizational Behavior and Human Decision Processes 88(2): 606–620.
• Levitt, Steven, and John A. List.  2007. “What Do Laboratory Experiments Measuring Social Preferences Reveal about the Real World?” Journal of Economic Perspectives 21(2): 153–74.
• Mas, Alexandre, and Enrico Moretti.  2009. “Peers at Work.” American Economic Review 99(1): 112–45.
• Michaelson, Larry K., Warren E. Watson, and Robert H. Black.  1989. “A Realistic Test of Individual versus Group Consensus Decision Making.” Journal of Applied Psychology 74(5): 834–39.
• Schopler, John, Chester A. Insko, Jennifer Wieselquist, Michael Pemberton, Betty Witcher, Rob Kozar, Chris Roddenberry, and Tim Wildschut.  2001. “When Groups Are More Competitive Than Individuals: The Domain of the Discontinuity Effect.” Journal of Personality and Social Psychology 80(4): 632–44.
• Sheremeta, Roman M., and Jingjing Zhang. 2010. “Can Groups Solve the Problem of Over-bidding in Contests?”  Social Choice and Welfare 35(2): 175–97.
• Song, Fei. 2008. “Trust and Reciprocity Behavior and Behavioral Forecasts: Individuals versus Group-Representatives.”  Games and Economic Behavior 62(2): 675–96.
• Surowiecki, James. 2004. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Doubleday.
• Sutter, Matthias. 2005. “Are Four Heads Better Than Two? An Experimental Beauty-Contest Game with Teams of Different Size.”  Economics Letters 88(1): 41–46.
• Tversky, Amos, and Daniel Kahneman.  1983. “Extensional versus Intuitive Reasoning: The Conjunction Fallacy in Probability Judgment.” Psychological Review 90(40): 293–315.

==cited by==

1. Ananish Chaudhuri, Tirnud Paichayontvijit, Lifeng Shen. 2012. Gender differences in trust and trustworthiness: Individuals, single sex and mixed sex groups. Journal of Economic Psychology.

 

Do We Follow Private Information when We Should? Laboratory Evidence on Naive Herding

Date:	2012-02
By:	Christoph March (PSE – Paris-Jourdan Sciences Economiques – CNRS : UMR8545 – Ecole des Hautes Etudes en Sciences Sociales (EHESS) – Ecole des Ponts ParisTech – Ecole Normale Supérieure de Paris – ENS Paris – INRA, EEP-PSE – Ecole d’Économie de Paris – Paris School of Economics – Ecole d’Économie de Paris) Sebastian Krügel (Max Planck Institute of Economics – Max Planck Institute of Economics) Anthony Ziegelmeyer (Max Planck Institute of Economics – Max Planck Institute of Economics)
URL:	http://d.repec.org/n?u=RePEc:hal:psewpa:halshs-00671378&r=net
	We investigate whether experimental participants follow their private information and contradict herds in situations where it is empirically optimal to do so. We consider two sequences of players, an observed and an unobserved sequence. Observed players sequentially predict which of two options has been randomly chosen with the help of a medium quality private signal. Unobserved players predict which of the two options has been randomly chosen knowing previous choices of observed and with the help of a low, medium or high quality signal. We use preprogrammed computers as observed players in half the experimental sessions. Our new evidence suggests that participants are prone to a ‘social-confirmation’ bias and it gives support to the argument that they naively believe that each observable choice reveals a substantial amount of that person’s private information. Though both the ‘overweighting-of-private-information’ and the ‘social-con firmation’ bias coexist in our data, participants forgo much larger parts of earnings when herding naively than when relying too much on their private information. Unobserved participants make the empirically optimal choice in 77 and 84 percent of the cases in the human-human and computer-human treatment which suggests that social learning improves in the presence of lower behavioral uncertainty.
Keywords:	Information cascades ; Laboratory Experiments ; Naive herding

 

Distinguishing informational cascades from herd behavior in the laboratory

Distinguishing informational cascades from herd behavior in the laboratory

Bo ̆açhan Çelen and Shachar Kariv (2004) The American Economic Review, 2004 – ingentaconnect.com

… circumstances. While the terms informational cascade and herd behavior are used

interchange- ably in the literature, Lones Smith and Peter N. Sørensen (2000)

emphasize that there is a significant difference between them. …

Smith and Sørensen (2000) emphasize that there is a significant difference between them. An informational cascade is said to occur when an infinite sequence of individuals ignore their private information when making a decision, whereas herd behavior occurs when an infinite sequence of individuals make an identical decision, not necessarily ignoring their private information.

被引用 139 次 – 相關文章 – 全部共 27 個版本; berkeley.edu 提供的 [PDF]

由Lones Smith and Peter N.Sørensen 2000 – Google 學術搜尋.