Kamwoo Lee | University of Virginia (original) (raw)

Papers by Kamwoo Lee

Computational and Mathematical Organization Theory, 2021

World Development

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibi... more Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and crosscountry estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our crosscountry estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low-and middle-income countries.

2017 Winter Simulation Conference (WSC), 2017

This study uses agent-based simulation with human settlement patterns to model belief revision an... more This study uses agent-based simulation with human settlement patterns to model belief revision and information exchange about health care options. We adopt two recent microeconomic theories based on Bayesian Network formulations for individual belief update then examine the macro-level effects of the belief revision process. This model tries to explain traditional healing usage at the village and regional level while providing a causal mechanism with a single conceptual factor, mobility, at the individual level. The resulting simulation estimates the dependency on traditional healing in villages in Limpopo, South Africa, and the estimates are validated with empirical data.

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibi... more Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

J. Artif. Soc. Soc. Simul., 2018

In this paper, we present a novel method to predict Bitcoin price movement utilizing inverse rein... more In this paper, we present a novel method to predict Bitcoin price movement utilizing inverse reinforcement learning (IRL) and agent-based modeling (ABM). Our approach consists of predicting the price through reproducing synthetic yet realistic behaviors of rational agents in a simulated market, instead of estimating relationships between the price and price-related factors. IRL provides a systematic way to find the behavioral rules of each agent from Blockchain data by framing the trading behavior estimation as a problem of recovering motivations from observed behavior and generating rules consistent with these motivations. Once the rules are recovered, an agent-based model creates hypothetical interactions between the recovered behavioral rules, discovering equilibrium prices as emergent features through matching the supply and demand of Bitcoin. One distinct aspect of our approach with ABM is that while conventional approaches manually design individual rules, our agentsâ€™ rules ...

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibi... more Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

Comput. Math. Organ. Theory, 2021

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibi... more Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

Winter Simulation Conference, 2017

Agent-based modeling (ABM) assumes that behavioral rules affecting an agent's states and actions ... more Agent-based modeling (ABM) assumes that behavioral rules affecting an agent's states and actions are known. However, discovering these rules is often challenging and requires deep insight about an agent's behaviors. Inverse reinforcement learning (IRL) can complement ABM by providing a systematic way to find behavioral rules from data. IRL frames learning behavioral rules as a problem of recovering motivations from observed behavior and generating rules consistent with these motivations. In this paper, we propose a method to construct an agent-based model directly from data using IRL. We explain each step of the proposed method and describe challenges that may occur during implementation. Our experimental results show that the proposed method can extract rules and construct an agent-based model with rich but concise behavioral rules for agents while still maintaining aggregate-level properties.

Social, Cultural, and Behavioral Modeling

Journal of Artificial Societies and Social Simulation

Computational and Mathematical Organization Theory, 2021

World Development

2017 Winter Simulation Conference (WSC), 2017

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibi... more Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

J. Artif. Soc. Soc. Simul., 2018

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibi... more Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

Comput. Math. Organ. Theory, 2021

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibi... more Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

Winter Simulation Conference, 2017

Social, Cultural, and Behavioral Modeling

Journal of Artificial Societies and Social Simulation