Social Media and Misleading Information in a Democracy: A Mechanism Design Approach

Dave, Aditya; Chremos, Ioannis Vasileios; Malikopoulos, Andreas A.

doi:10.1109/tac.2021.3087466

Cited by 19 publications

(7 citation statements)

References 17 publications

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“…Ongoing research includes applying this framework in problems related to emerging mobility systems [106] and social networks [107]. A potential direction for future research should consider relaxing Assumption 1 and investigating appropriate online learning algorithms for the information state in conjunction with -convergence criteria, i.e., identifying the learning error which might be acceptable for Theorem 3 to hold.…”

Section: Concluding Remarks and Discussionmentioning

confidence: 99%

Separation of Learning and Control for Cyber-Physical Systems

Malikopoulos¹

2021

Preprint

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Most cyber-physical systems (CPS) encounter a large volume of data with a dynamic nature which is added to the system gradually in real time and not altogether in advance. Therefore, neither traditional supervised (or unsupervised) learning nor typical model-based control approaches can effectively facilitate optimal solutions with performance guarantees. In this article, we provide a theoretical framework that yields optimal control strategies at the intersection of control theory and learning. In the proposed framework, we use the actual CPS, i.e., the "true" CPS that we seek to optimally control online, in parallel with a model of the CPS that we have available. We institute an information state which is the conditional joint probability distribution of the states of the model and the actual CPS given all data available up until each instant of time. We use this information state along with the CPS model to derive offline separated control strategies. Since the strategies are derived offline, the state of the actual CPS is not known, i.e., the model cannot capture the dynamics of the actual CPS due to the complexity of the system, and thus the optimal strategy of the model is parameterized with respect to the state of the actual CPS. However, the control strategy and the process of estimating the information state are separated. Therefore, we can learn the information state of the system online while we operate the model and the actual CPS. We show that after the information state becomes known online through learning, the separated control strategy of the model derived offline is optimal for the actual CPS. We illustrate the proposed framework in a dynamic system consisting of two subsystems with a delayed sharing information structure.

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Section: Concluding Remarks and Discussionmentioning

confidence: 99%

Separation of Learning and Control for Cyber-Physical Systems

Malikopoulos¹

2021

Preprint

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“…In (39), recall that the uncertain variable Πt is an L-invertible function of M t and thus, the conditional range [[M t |π t ]] satisfies (3). Then, we use (35) once more to state that sup…”

Section: B Properties Of Approximate Information Statesmentioning

confidence: 99%

“…Decision-making under incomplete information is a fundamental problem in modern engineering applications involving cyber-physical systems [1], e.g., connected and automated vehicles [2], social media platforms [3], and robot swarms [4]. In such applications, an agent is often required to sequentially select control inputs to a dynamic system using only partial observations at each instance of time, while simultaneously accounting for uncontrolled disturbances that can interfere with the system's evolution.…”

Section: Introductionmentioning

confidence: 99%

Approximate Information States for Worst-Case Control and Learning in Uncertain Systems

Dave¹,

Nishanth²,

Malikopoulos³

2023

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In this paper, we investigate discrete-time decisionmaking problems in uncertain systems with partially observed states. We consider a non-stochastic model, where uncontrolled disturbances acting on the system take values in bounded sets with unknown distributions. We present a general framework for decision-making in such problems by developing the notions of information states and approximate information states. In our definition of an information state, we introduce conditions to identify for an uncertain variable sufficient to construct a dynamic program (DP) that computes an optimal strategy. We show that many information states from the literature on worstcase control actions, e.g., the conditional range, are examples of our more general definition. Next, we relax these conditions to define approximate information states using only output variables, which can be learned from output data without knowledge of system dynamics. We use this notion to formulate an approximate DP that yields a strategy with a bounded performance loss. Finally, we illustrate the application of our results in control and reinforcement learning using numerical examples.

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“…Mechanism design has broad applications spanning different fields, including microeconomics, social choice theory, and control engineering. Applications in engineering include communication networks [40], social networks [41], transportation routing [42], online advertising [43], smart grid [44], multi-agent systems [45], and resource allocation problems [46]. Due to the economic nature of congestion in transportation, auctioning has been also proposed [47] to create a market of tolls in a network of roads.…”

Section: Step 1: Socially-optimal Management Of Travel Demandmentioning

confidence: 99%

A Control Framework for Socially-Optimal Emerging Mobility Systems

Malikopoulos¹

2023

Preprint

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In this manuscript, we provide an online learning framework that will aim at distributing travel demand in a given transportation network resulting in a socially-optimal mobility system that travelers would be willing to accept. It is expected that connected and automated vehicles (CAVs) will gradually penetrate the market in ways that will improve safety and transportation efficiency over the next several years. However, we anticipate that efficient transportation and travel cost reduction might alter human travel behavior causing rebound effects, e.g., by improving effi-ciency, travel cost is decreased, hence willingness-to-travel is increased. The latter would increase overall vehicle miles traveled, which in turn might negate the benefits in terms of energy and travel time. Our aim is to develop a holistic and rigorous framework to capture the societal impact of CAVs and provide solutions that mitigate any potential rebound effects, e.g., increased vehicle miles traveled, increased travel demand, empty vehicle trips, while enhancing accessibility, safety, and equity in transportation.

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Social Media and Misleading Information in a Democracy: A Mechanism Design Approach

Cited by 19 publications

References 17 publications

Separation of Learning and Control for Cyber-Physical Systems

Separation of Learning and Control for Cyber-Physical Systems

Approximate Information States for Worst-Case Control and Learning in Uncertain Systems

A Control Framework for Socially-Optimal Emerging Mobility Systems

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