Consumer Price Search and Platform Design in Internet Commerce

Consumer Price Search and Platform Design in Internet Commerce

==noted by yinung==

這個網站搜尋、比價的主題很久了, 但仍是有趣, 也是有人用 law of one price 的觀念來看, 但裡頭的實驗怎麼做, 再了解。

Date:	2014-08
By:	Michael Dinerstein Liran Einav Jonathan Levin Neel Sundaresan
URL:	http://d.repec.org/n?u=RePEc:nbr:nberwo:20415&r=net
	Search frictions can explain why the “law of one price" fails in retail markets and why even firms selling commodity products have pricing power. In online commerce, physical search costs are low, yet price dispersion is common. We use browsing data from eBay to estimate a model of consumer search and price competition when retailers offer homogeneous goods. We find that retail margins are on the order of 10%, and use the model to analyze the design of search rankings. Our model explains most of the effects of a major re-design of eBay’s product search, and allows us to identify conditions where narrowing consumer choice sets can be pro-competitive. Finally, we examine a subsequent A/B experiment run by eBay that illustrates the greater difficulties in designing search algorithms for differentiated products, where price is only one of the relevant product attributes.
JEL:	D12 D22 D83 L13 L86

Turf Wars

Turf Wars

Date:	2014-10
By:	Herrera, Helios (HEC Montreal) Reuben, Ernesto (Columbia University) Ting, Michael M. (Columbia University)
URL:	http://d.repec.org/n?u=RePEc:iza:izadps:dp8585&r=net
	Turf wars commonly occur in environments where competition undermines collaboration. We develop a game theoretic model and experimental test of turf wars. The model explores how team production incentives ex post affect team formation decisions ex ante. In the game, one agent decides whether to share jurisdiction over a project with other agents. Agents with jurisdiction decide whether to exert effort and receive a reward based on their relative performance. Hence, sharing can increase joint production but introduces competition for the reward. We find that collaboration has a non-monotonic relationship with both productivity and rewards. The laboratory experiment confirms the model’s main predictions. We also explore extensions of the basic model, including one where each agent’s productivity is private information.
Keywords:	turf war, bureaucracy, jurisdiction, competition, information withholding
JEL:	D73 D74 D82

Anonymous social influence

Anonymous social influence

Date:	2013
By:	Manuel Foerster (CES – Centre d’économie de la Sorbonne – CNRS : UMR8174 – Université Paris I – Panthéon-Sorbonne, CORE – Center of Operation Research and Econometrics [Louvain] – Université Catholique de Louvain (UCL) – Belgique) Michel Grabisch (CES – Centre d’économie de la Sorbonne – CNRS : UMR8174 – Université Paris I – Panthéon-Sorbonne, EEP-PSE – Ecole d’Économie de Paris – Paris School of Economics – Ecole d’Économie de Paris) Agnieszka Rusinowska (CES – Centre d’économie de la Sorbonne – CNRS : UMR8174 – Université Paris I – Panthéon-Sorbonne, EEP-PSE – Ecole d’Économie de Paris – Paris School of Economics – Ecole d’Économie de Paris)
URL:	http://d.repec.org/n?u=RePEc:hal:journl:halshs-00913235&r=net
	We study a stochastic model of influence where agents have “yes" or “no" inclinations on some issue, and opinions may change due to mutual influence among the agents. Each agent independently aggregates the opinions of the other agents and possibly herself. We study influence processes modeled by ordered weighted averaging operators, which are anonymous: they only depend on how many agents share an opinion. For instance, this allows to study situations where the influence process is based on majorities, which are not covered by the classical approach of weighted averaging aggregation. We find a necessary and sufficient condition for convergence to consensus and characterize outcomes where the society ends up polarized. Our results can also be used to understand more general situations, where ordered weighted averages are only used to some extent. Furthermore, we apply our results to fuzzy linguistic quantifiers, i.e., expressions like “most" or “at least a few".
Keywords:	Influence; Anonymity; Ordered weighted averaging operator; Convergence; Consensus; Fuzzy linguistic quantifier

Methods of Identification in Social Networks

Methods of Identification in Social Networks

Date:	2014-08
By:	Bryan S. Graham
URL:	http://d.repec.org/n?u=RePEc:nbr:nberwo:20414&r=net
	Social and economic networks are ubiquitous, serving as contexts for job search, technology diffusion, the accumulation of human capital and even the formulation of norms and values. The systematic empirical study of network formation – the process by which agents form, maintain and dissolve links – within economics is recent, is associated with extraordinarily challenging modeling and identification issues, and is an area of exciting new developments, with many open questions. This article reviews prominent research on the empirical analysis of network formation, with an emphasis on contributions made by economists.
JEL:	C23 C25 D85