Efficient online exchange via fiat money

Schaar, Mihaela van der; Xu, Jie; Zame, William R.

doi:10.1007/s00199-013-0744-4

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…4, we know that monetary trading (with a rich set of possible status levels/money holdings and a sufficiently high discount factor) is nearly efficient in several settings (e.g. Berentsen 2002;Green and Zhou 2005;van der Schaar et al 2013;Olszewski and Safronov 2018). It is also the case that several authors have been able to find trading mechanisms that dominate monetary trading.…”

Section: Discussionmentioning

confidence: 99%

On the optimality of monetary trading

Carmona

2019

Econ Theory

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People have long been trading in a "monetary" way. This persistence of monetary trading suggests that it might be an efficient trading mechanism. We formalize this intuition in a random-matching, absence-of-double-coincidence-of-wants environment. The record-keeping technology is operated by an information-processing center which summarizes and updates individuals' past trading behavior in a binary variable. A trading mechanism consists of an updating rule and an individual trading behavior rule. To capture the difficulties in collecting and processing information about others' past behavior, we assume that the center faces costs, both fixed and variable, to operate. We show that (1) any non-autarkic equilibrium trading mechanism is, in terms of aggregate variables (such as consumption and frequency of trade), observationally equivalent to a monetary trading mechanism and (2) any non-autarkic optimal equilibrium trading mechanism is a monetary trading mechanism.

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Section: Discussionmentioning

confidence: 99%

On the optimality of monetary trading

Carmona

2019

Econ Theory

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“…For instance, it can be a payment made to CA j to display it to CA i's user, or it may be associated with the advertising loss CA i incurs by directing its user to CA j's website. When the cost is payment, it can be money, tokens [27] or Bitcoins [28]. Since this cost is bounded, without loss of generality we assume that d i j ∈ [0, 1] for all i, j ∈ M. In order make our model general, we also assume that there is a cost associated with recommending a type of content c ∈ C i , which is given by d i c ∈ [0, 1], for CA i.…”

Section: A User and Content Characteristicsmentioning

confidence: 99%

Contextual Online Learning for Multimedia Content Aggregation

Tekin

Schaar

2015

IEEE Trans. Multimedia

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The last decade has witnessed a tremendous growth in the volume as well as the diversity of multimedia content generated by a multitude of sources (news agencies, social media, etc.). Faced with a variety of content choices, consumers are exhibiting diverse preferences for content; their preferences often depend on the context in which they consume content as well as various exogenous events. To satisfy the consumers' demand for such diverse content, multimedia content aggregators (CAs) have emerged which gather content from numerous multimedia sources. A key challenge for such systems is to accurately predict what type of content each of its consumers prefers in a certain context, and adapt these predictions to the evolving consumers' preferences, contexts, and content characteristics. We propose a novel, distributed, online multimedia content aggregation framework, which gathers content generated by multiple heterogeneous producers to fulfill its consumers' demand for content. Since both the multimedia content characteristics and the consumers' preferences and contexts are unknown, the optimal content aggregation strategy is unknown a priori. Our proposed content aggregation algorithm is able to learn online what content to gather and how to match content and users by exploiting similarities between consumer types. We prove bounds for our proposed learning algorithms that guarantee both the accuracy of the predictions as well as the learning speed. Importantly, our algorithms operate efficiently even when feedback from consumers is missing or content and preferences evolve over time. Illustrative results highlight the merits of the proposed content aggregation system in a variety of settings.Index Terms-Content aggregation, distributed online learning, multi-armed bandits, social multimedia.

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“…They associate a price (in bandwidth) with each file and find a market equilibrium in the resulting economy. Later work by Aperjis et al does use a currency, but the focus remains on establishing market prices [2008] Subsequent to our work, Dandekar et al [2011] examined networks where each agent can issue his own scrip, Rahman et al [2010] proposed a protocol to automatically adjust credit policies to maintain stability, Humbert et al [2011] modeled scrip systems where multiple agents combine to provide service, van der Schaar et al [2013] studied how to set parameters in a way that is robust to noise, and Johnson et al [2014] showed that welfare can be improved if the system prefers volunteers with low amounts of scrip.…”

Section: Related Workmentioning

confidence: 99%

An Equilibrium Analysis of Scrip Systems

Kash

Friedman

Halpern

2015

ACM Trans. Econ. Comput.

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A game-theoretic model of scrip (artificial currency) systems is analyzed. It is shown that relative entropy can be used to characterize the distribution of agent wealth when all agents use threshold strategies-that is, they volunteer to do work iff they have below a threshold amount of money. Monotonicity of agents' bestreply functions is used to show that scrip systems have pure strategy equilibria where all agents use threshold strategies. An algorithm is given that can compute such an equilibrium and the resulting distribution of wealth.

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Efficient online exchange via fiat money

Cited by 14 publications

References 25 publications

On the optimality of monetary trading

On the optimality of monetary trading

Contextual Online Learning for Multimedia Content Aggregation

An Equilibrium Analysis of Scrip Systems

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