Social learning with heterogeneous agents and sequential decision making

Wang, Yunlong; Djurić, Petar M.

doi:10.1016/j.dsp.2015.02.016

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Recent innovations in communication technologies, coupled with the increased use of Internet and smartphones, greatly enhanced institutions' ability to gather and process an enormous amount of information on individual users on social networks or consumers in different platforms [1][2][3][4]. Today, many sources of information from shares on social networks to blogs, from intelligent device activities to security camera recordings are easily collectable.…”

Section: Introductionmentioning

confidence: 99%

An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach

Delibalta

Baruh

Kozat

2017

Journal of Electrical and Computer Engineering

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We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios.

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Section: Introductionmentioning

confidence: 99%

An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach

Delibalta

Baruh

Kozat

2017

Journal of Electrical and Computer Engineering

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“…Each sensor being an expected reward maximizer is in line with being rational; see[24]. See[28],[29] for models considering a mix of rational and irrational sensors 6. Here informativeness is in the sense of Blackwell[11]; see also Footnote 8.…”

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confidence: 99%

Controlled information fusion with risk-averse CVaR social sensors

Bhatt

Krishnamurthy

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Consider a multi-agent network comprised of riskaverse social sensors and a controller that jointly seek to estimate an unknown state of nature, given noisy measurements.The network of social sensors perform Bayesian social learning -each sensor fuses the information revealed by previous social sensors along with its private valuation using Bayes' rule -to optimize a local cost function. The controller sequentially modifies the cost function of the sensors by discriminatory pricing (control inputs) to realize long term global objectives.We formulate the stochastic control problem faced by the controller as a Partially Observed Markov Decision Process (POMDP) and derive structural results for the optimal control policy as a function of the risk-aversion factor in the Conditional Value-at-Risk (CVaR) cost function of the sensors. We show that the optimal price sequence when the sensors are riskaverse is a super-martingale; i.e, it decreases on average over time.

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