SSRN Author: Ozan CandoganOzan Candogan SSRN Content
https://www.ssrn.com/author=2329383
https://www.ssrn.com/rss/en-usTue, 16 Aug 2022 01:06:28 GMTeditor@ssrn.com (Editor)Tue, 16 Aug 2022 01:06:28 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Correlated Cluster-Based Randomized Experiments: Robust Variance MinimizationExperimentation is prevalent in online marketplaces and social networks to assess the effectiveness of new market intervention. To mitigate the interference among users in an experiment, a common practice is to use a cluster-based experiment, where the designer partitions the market into loosely connected clusters and assigns all users in the same cluster to the same variant (treatment or control). Given the experiment, we assume an unbiased Horvitz-Thompson estimator is used to estimate the total market effect of the treatment. We consider the optimization problem of choosing (correlated) randomized assignments of clusters to treatment and control to minimize the worst-case variance of the estimator under a constraint that the marginal assignment probability is q \in (0,1) for all clusters. This problem can be formulated as a linear program where both the number of decision variables and constraints are exponential in the number of clusters---and hence is generally computationally ...
https://www.ssrn.com/abstract=3852100
https://www.ssrn.com/2173075.htmlMon, 15 Aug 2022 09:10:19 GMTNew: Managing Resources for Shared Micromobility: Approximate Optimality in Large-Scale SystemsWe consider the problem of managing resources in shared micromobility systems (bike-sharing and scooter-sharing). An important task in managing such systems is periodic repositioning/recharging/sourcing units to avoid stockouts or excess inventory at nodes with unbalanced flows. We consider a discrete-time model: each period begins with an initial inventory at each node in the network, and then customers (demand) materialize at the nodes. Each customer picks up a unit at the origin node and drops it off at a randomly sampled destination node with an origin-specific probability distribution. We model the above network inventory management problem as an infinite horizon discrete-time discounted Markov Decision Process and prove the asymptotic optimality of a novel mean-field approximation to the original MDP as the number of stations becomes large. To compute an approximately optimal policy for the mean-field dynamics, we provide an algorithm with a running time that is logarithmic in ...
https://www.ssrn.com/abstract=4155841
https://www.ssrn.com/2162939.htmlTue, 19 Jul 2022 17:38:17 GMTREVISION: Correlated Cluster-Based Randomized Experiments: Robust Variance MinimizationExperimentation is prevalent in online marketplaces and social networks to assess the effectiveness of new market intervention. To mitigate the interference among users in an experiment, a common practice is to use a cluster-based experiment, where the designer partitions the market into loosely connected clusters and assigns all users in the same cluster to the same variant (treatment or control). Given the experiment, we assume an unbiased Horvitz-Thompson estimator is used to estimate the total market effect of the treatment. We consider the optimization problem of choosing (correlated) randomized assignments of clusters to treatment and control to minimize the worst-case variance of the estimator under a constraint that the marginal assignment probability is q \in (0,1) for all clusters. This problem can be formulated as a linear program where both the number of decision variables and constraints are exponential in the number of clusters---and hence is generally computationally ...
https://www.ssrn.com/abstract=3852100
https://www.ssrn.com/2150125.htmlMon, 13 Jun 2022 14:53:22 GMTREVISION: Social Learning Under Platform Influence: Consensus and Persistent DisagreementIndividuals increasingly rely on social networking platforms to form opinions. However, these platforms typically aim to maximize engagement, which may not align with social good. In this paper, we introduce an opinion dynamics model where agents are connected in a social network, and update their opinions based on their neighbors’ opinions and on the content shown to them by the platform. We focus on a stochastic block model with two blocks, where the initial opinions of the individuals in different blocks are different. We prove that for large and dense enough networks the trajectory of opinion dynamics in such networks can be approximated well by a simple two-agent system. The latter admits tractable analytical analysis, which we leverage to provide interesting insights into the platform’s impact on the social learning outcome in our original two-block model. Specifically, by using our approximation result, we show that agents’ opinions approximately converge to some limiting ...
https://www.ssrn.com/abstract=3675712
https://www.ssrn.com/2111857.htmlFri, 04 Mar 2022 01:16:25 GMTREVISION: Social Learning Under Platform Influence: Consensus and Persistent DisagreementIndividuals increasingly rely on social networking platforms to form opinions. However, these platforms typically aim to maximize engagement, which may not align with social good. In this paper, we introduce an opinion dynamics model where agents are connected in a social network, and update their opinions based on their neighbors’ opinions and on the content shown to them by the platform. We focus on a stochastic block model with two blocks, where the initial opinions of the individuals in different blocks are different. We prove that for large and dense enough networks the trajectory of opinion dynamics in such networks can be approximated well by a simple two-agent system. The latter admits tractable analytical analysis, which we leverage to provide interesting insights into the platform’s impact on the social learning outcome in our original two-block model. Specifically, by using our approximation result, we show that agents’ opinions approximately converge to some limiting ...
https://www.ssrn.com/abstract=3675712
https://www.ssrn.com/2109212.htmlSat, 26 Feb 2022 14:43:03 GMTREVISION: Social Learning Under Platform Influence: Consensus and Persistent DisagreementIndividuals increasingly rely on social networking platforms to form opinions. However, these platforms typically aim to maximize engagement, which may not align with social good. In this paper, we introduce an opinion dynamics model where agents are connected in a social network, and update their opinions based on their neighbors’ opinions and on the content shown to them by the platform. We focus on a stochastic block model with two blocks, where the initial opinions of the individuals in different blocks are different. We prove that for large and dense enough networks the trajectory of opinion dynamics in such networks can be approximated well by a simple two-agent system. The latter admits tractable analytical analysis, which we leverage to provide interesting insights into the platform’s impact on the social learning outcome in our original two-block model. Specifically, by using our approximation result, we show that agents’ opinions approximately converge to some limiting ...
https://www.ssrn.com/abstract=3675712
https://www.ssrn.com/2109211.htmlSat, 26 Feb 2022 14:42:48 GMTREVISION: Optimal Disclosure of Information to Privately Informed AgentsWe study information design settings where the designer controls information about a state, and there are multiple agents interacting in a game who are privately informed about their types. Each agent’s utility depends on all agents’ types and actions, as well as (linearly) on the state. To optimally screen the agents, the designer first asks agents to report their types and then sends a private action recommendation to each agent whose distribution depends on all reported types and the state. We show that there always exists an optimal mechanism which is laminar partitional. Such a mechanism partitions the state space for each type profile and recommends the same action profile for states that belong to the same partition element. Furthermore, the convex hulls of any two partition elements are such that either one contains the other or they have an empty intersection. In the single-agent case, each state is either perfectly revealed or lies in an interval in which the number of ...
https://www.ssrn.com/abstract=3773326
https://www.ssrn.com/2104158.htmlFri, 11 Feb 2022 03:52:15 GMTREVISION: Persuasion in Networks: Public Signals and CoresWe consider a setting where agents in a social network take binary actions that exhibit local strategic complementarities. Their payoffs are affine and increasing in an underlying real-valued state of the world. An information designer commits to a signaling mechanism that publicly reveals a signal that is potentially informative about the state. She wants to maximize the expected number of agents who take action 1. We study the structure and design of optimal public signaling mechanisms.<br><br>The designer’s payoff is an increasing step function of the posterior mean (of the state) induced by the realization of her signal. We provide a convex optimization formulation and an algorithm that obtain an optimal public signaling mechanism whenever the designer’s payoff admits this structure. This structure is prevalent, making our formulation and results useful well beyond persuasion in networks. In our problem, the step function is characterized in terms of the cores of the underlying ...
https://www.ssrn.com/abstract=3346144
https://www.ssrn.com/2098604.htmlThu, 27 Jan 2022 01:44:02 GMTREVISION: Optimal Disclosure of Information to Privately Informed AgentsWe study information design settings where the designer controls information about a state, and there are multiple agents interacting in a game who are privately informed about their types. Each agent’s utility depends on all agents’ types and actions, as well as (linearly) on the state. To optimally screen the agents, the designer first asks agents to report their types and then sends a private action recommendation to each agent whose distribution depends on all reported types and the state. We show that there always exists an optimal mechanism which is laminar partitional. Such a mechanism partitions the state space for each type profile and recommends the same action profile for states that belong to the same partition element. Furthermore, the convex hulls of any two partition elements are such that either one contains the other or they have an empty intersection. In the single-agent case, each state is either perfectly revealed or lies in an interval in which the number of ...
https://www.ssrn.com/abstract=3773326
https://www.ssrn.com/2098556.htmlThu, 27 Jan 2022 01:18:29 GMTREVISION: Optimal Disclosure of Information to Privately Informed AgentsWe study information design settings where the designer controls information about a state, and there are multiple agents interacting in a game who are privately informed about their types. Each agent’s utility depends on all agents’ types and actions, as well as (linearly) on the state. To optimally screen the agents, the designer first asks agents to report their types and then sends a private action recommendation to each agent whose distribution depends on all reported types and the state. We show that there always exists an optimal mechanism which is laminar partitional. Such a mechanism partitions the state space for each type profile and recommends the same action profile for states that belong to the same partition element. Furthermore, the convex hulls of any two partition elements are such that either one contains the other or they have an empty intersection. In the single-agent case, each state is either perfectly revealed or lies in an interval in which the number of ...
https://www.ssrn.com/abstract=3773326
https://www.ssrn.com/2098555.htmlThu, 27 Jan 2022 01:18:28 GMTREVISION: Controlling Epidemic Spread: Reducing Economic Losses with Targeted ClosuresData on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions we study a spatial epidemic model that explicitly accounts for population movements, and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020), our main model illustrates that appropriate targeting achieves a reduction in ...
https://www.ssrn.com/abstract=3590621
https://www.ssrn.com/2078059.htmlThu, 18 Nov 2021 18:49:15 GMTREVISION: Near-Optimal Experimental Design for Networks: Independent Block RandomizationMotivated by the prevalence of experimentation in online platforms and social networks, we consider the problem of designing randomized experiments to assess the effectiveness of a new market intervention for a network of users. An experiment assigns each user to either the treatment or the control group. The outcome of each user depends on her assignment as well as the assignments of her neighbors. Given the experiment, the unbiased Horvitz-Thompson estimator is used to estimate the total market effect of the treatment. The decision maker chooses randomized assignments of users to treatment and control, in order to minimize the worst-case variance of this estimator. We focus on networks that can be partitioned into communities, where the users in the same community are densely connected, and users from different communities are only loosely connected. In such settings, it is almost without loss to assign all users in the same community to the same variant (treatment or control). ...
https://www.ssrn.com/abstract=3852100
https://www.ssrn.com/2064459.htmlWed, 29 Sep 2021 23:20:37 GMTREVISION: Optimal Disclosure of Information to Privately Informed AgentsWe study information design settings where the designer controls information about a state, and there are multiple agents interacting in a game who are privately informed about their types. Each agent’s utility depends on all agents’ types and actions, as well as (linearly) on the state. To optimally screen the agents, the designer first asks agents to report their types and then sends a private action recommendation to each agent whose distribution depends on all reported types and the state. We show that there always exists an optimal mechanism which is laminar partitional. Such a mechanism partitions the state space for each type profile and recommends the same action profile for states that belong to the same partition element. Furthermore, the convex hulls of any two partition elements are such that either one contains the other or they have an empty intersection. In the single-agent case, each state is either perfectly revealed or lies in an interval in which the number of ...
https://www.ssrn.com/abstract=3773326
https://www.ssrn.com/2061724.htmlTue, 21 Sep 2021 01:31:28 GMTREVISION: Near-Optimal Experimental Design for Networks: Independent Block RandomizationMotivated by the prevalence of experimentation in online platforms and social networks, we consider the problem of designing randomized experiments to assess the effectiveness of a new market intervention for a network of users. An experiment assigns each user to either the treatment or the control group. The outcome of each user depends on her assignment as well as the assignments of her neighbors. Given the experiment, the unbiased Horvitz-Thompson estimator is used to estimate the total market effect of the treatment. The decision maker chooses randomized assignments of users to treatment and control, in order to minimize the worst-case variance of this estimator. We focus on networks that can be partitioned into communities, where the users in the same community are densely connected, and users from different communities are only loosely connected. In such settings, it is almost without loss to assign all users in the same community to the same variant (treatment or control). ...
https://www.ssrn.com/abstract=3852100
https://www.ssrn.com/2051421.htmlTue, 17 Aug 2021 00:04:52 GMT