Evolution of markets: a simulation with centralized, decentralized and posted offer formats

Rud, Olga A.; Rabanal, Jean Paul

doi:10.1007/s00191-016-0488-y

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…This link, however, has rarely been made explicit for the case of market selection. An exception is Rud and Rabanal (2018), who conduct a simulation analysis of market selection with trader heterogeneity relying on individual-level replicator dynamics to determine the time allocation across different market types. 26 Note that the highest price and the highest average price are displayed explicitly in the market selection stage, whereas the ratio of buyers to sellers is not shown explicitly but can be computed fairly easily as both the number of buyers and sellers at each institution is shown on screen.…”

Section: Results: Switching Behaviormentioning

confidence: 99%

“…This in turn led to substantial research on the evolution of market rules (see e.g. Rud and Rabanal 2018). Starting with the seminal contribution of Hayek (1967) it has been argued that traders, if left to their own devices, choose the most efficient trading institution that the good at hand allows for.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, though, these investigations have not been connected to the functioning of markets.4 We speak of "market institutions" for consistency with the previous literature. Other authors have used the term "market formats" (e.g Rud and Rabanal 2018)…”

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confidence: 99%

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Do traders learn to select efficient market institutions?

2021

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When alternative market institutions are available, traders have to decide both where and how much to trade. We conducted an experiment where traders decided first whether to trade in an (efficient) double-auction institution or in a posted-offers one (favoring sellers), and second how much to trade. When sellers face decreasing returns to scale (increasing production costs), fast coordination on the double-auction occurs, with the posted-offers institution becoming inactive. In contrast, under constant returns to scale, both institutions remain active and coordination is slower. The reason is that sellers trade off higher efficiency in a market with dwindling profits for biased-up profits in a market with vanishing customers. Hence, efficiency alone might not be sufficient to guarantee coordination on a single market institution if the surplus distribution is asymmetric. Trading behavior approaches equilibrium predictions (market clearing) within each institution, but switching behavior across institutions is explained by simple rules of thumb, with buyers chasing low prices and sellers considering both prices and trader ratios.

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Section: Results: Switching Behaviormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Do traders learn to select efficient market institutions?

2021

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Banned from the sharing economy: an agent-based model of a peer-to-peer marketplace for consumer goods and services

Querbes

2017

J Evol Econ

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The emergence of profit-based online platforms related to the Sharing Economy, such as BlaBlaCar and Airbnb, provides new means for end users to create an income from their possessions. With this opportunity, participants have to make strategic economic decisions despite limited formal expertise and information. Decentralization (using digital technologies) and reputation (using user reviews) are the core mechanisms chosen by these platforms to mitigate these limitations and to work efficiently as online matchmakers. We test the performance of these two mechanisms by studying the allocative efficiency (in terms of value and volume of transactions) of simulated marketplaces under different types of motivation from the participants and control from the platforms. As a result, we find an inverted-U relationship between the decision-making leeway available to the participants and the platform’s allocative efficiency. From the participants’ perspectives, too much freedom or too many barriers lead to market failures affecting specific participants: low-end consumers are banned from the marketplace while high-end providers experience lower levels of activity. As governance advice for these platforms, we show the limitations of promoting these platforms on the sole motive of monetary rewards.

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