Revealed Preference Tests of the Cournot Model

Carvajal, Andrés; Deb, Rahul; Fenske, James; Quah, John

doi:10.3982/ecta10175

Cited by 53 publications

(44 citation statements)

References 27 publications

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“…Clearly the agents (local decision Figure 9: The social learning region for the risk-aversion parameter α ∈ (0, 1]. It can be seen that the social learning region is absent when agents are sufficiently risk-averse and is larger when the stock value is known to change, i.e, P I. makers) and market observer interact -the local decisions a k taken by the agents determines the public belief π k and hence determines decision u k of the market observer via (35). Figure 10: The value function V(π) and the double threshold optimal policy µ * (π) are plotted over π (2).…”

Section: Nonconvex Stopping Set For Market Shock Detectionmentioning

confidence: 99%

“…These include ARMA time series models [75], multivariate linear regression models [153], hidden Markov models [95], normal distribution fitting [60], and parametric model fitting [156,157]. Though all 34 YouTube website for helping with channels 35 This could also be due to the fact gaming videos account for 70% of the videos in the dataset. 36 We used a topic model to obtain the main tags.…”

Section: Social Sensor Engagement Dynamics With Youtube Videosmentioning

confidence: 99%

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Dynamics of Information Diffusion and Social Sensing

Krishnamurthy

Hoiles²

2018

Cooperative and Graph Signal Processing

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Statistical inference using social sensors is an area that has witnessed remarkable progress in the last decade. It is relevant in a variety of applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.

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Section: Nonconvex Stopping Set For Market Shock Detectionmentioning

confidence: 99%

Section: Social Sensor Engagement Dynamics With Youtube Videosmentioning

confidence: 99%

Dynamics of Information Diffusion and Social Sensing

Krishnamurthy

Hoiles²

2018

Cooperative and Graph Signal Processing

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“…are, respectively, the correct (incest free) private belief for node n and the correct after-action public belief. If agent n does not use π 0 n− , then incest can propagate; for example if agent n naively uses (29).…”

Section: Data Incest Model and Social Influencementioning

confidence: 99%

“…Just as with the utility maximization budget constraint in (34), the budget constraint p t x i ≤ I i t in (38) models the total amount of resources available to the social sensor for selecting the response x i t to the external influence p t . The detection test for Nash rationality (Definition 4.2) has been used in [29] to detect if oil producing countries are collusive, and in [44] for the analysis of household consumption behaviour.…”

Section: 2mentioning

confidence: 99%

Information diffusion in social sensing

Krishnamurthy¹,

Hoiles²

2016

NACO

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Statistical inference using social sensors is an area that has witnessed remarkable progress in the last decade. It is relevant in a variety of applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This paper presents a tutorial description of three important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models in online reputation systems are presented. Finally, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. All three topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this paper stem from recent developments in computer-science, economics, psychology and electrical engineering.

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“…This is always the first and, sometimes, the most important issue that a piece of RP analysis must resolve and it will have an impact on whether, and how, a model could be tested through a set of RP conditions. For example, in developing RP tests for an oligopoly model, one could imagine data being generated by changes to the demand functions, with firms' cost functions staying fixed in the observation period (as in Carvajal et al 2013), or the opposite, where demand functions are stable, while cost functions change and generate the data. What is reasonable depends on the context and the data that are, or are likely to be, available when implementing the test.…”

mentioning

confidence: 99%

Symposium on revealed preference analysis

Galichon

Quah

2013

Econ Theory

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Almost invariably, economic models postulate that agents behave according to some type of maximizing behavior. This is true even of models in behavioral economics, though agents in those settings may be unsophisticated in some way or have preferences that depart from classical assumptions. Revealed preference (RP) analysis investigates the observable implications of economic models and the extent to which the objects of a model, for example, agents' preferences, can be inferred from data. While RP analysis in this broad sense applies, of course, to a very large part of empirical investigation in economics, that literature which is typically understood by its practitioners as revealed preference analysis has more specific features: In general, the aim of the analysis is not merely to identify and test some empirical implications of a model, but to identify all the observable implications of a model. These implications take the form of restrictions on the data that could, at least in principle, be checked. Since the models being considered are usually sufficiently general that its objects are restricted by qualitative, rather than functional-form or parametric conditions, the same is true of the restrictions on the data.

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Revealed Preference Tests of the Cournot Model

Cited by 53 publications

References 27 publications

Dynamics of Information Diffusion and Social Sensing

Dynamics of Information Diffusion and Social Sensing

Information diffusion in social sensing

Symposium on revealed preference analysis

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