Marketing and Poverty Alleviation: Synergizing Research, Education, and Outreach Through the Subsistence Marketplaces Approach

In this article, we describe our journey through the creation and development of the stream of subsistence marketplaces, summarize our learning, and discuss implications at the intersection of the field of Marketing and poverty alleviation. Distinct from macro level economic research in impoverished contexts, or mid-level approaches, such as the base of the pyramid (BOP) approach in business strategy, this approach is rooted at the micro-level, enabling bottom up understanding of buyer and seller. The term, subsistence marketplaces, reflects understanding these contexts in their own right, not just as markets to sell to, but as individuals, communities, consumers, entrepreneurs, and marketplaces to learn from

Intelligent agents in electronic markets for information goods: customization, preference revelation and pricing

Electronic commerce has enabled the use of intelligent agent technologies that can evaluate buyers, customize products, and price in real-time. Our model of an electronic market with customizable products analyzes the pricing, profitability and welfare implications of agent-based technologies that price dynamically based on product preference information revealed by consumers. We find that in making the trade-off between better prices and better customization, consumers invariably choose less-than-ideal products. Furthermore, this trade-off has a higher impact on buyers on the higher end of the market and causes a transfer of consumer surplus towards buyers with a lower willingness to pay. As buyers adjust their product choices in response to better demand agent technologies, seller revenues decrease since the gains from better buyer information are dominated by the lowering of the total value created from the transactions. We study the strategic and welfare implications of these findings, and discuss managerial and technology development guidelines.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

The Impact of Intelligent Agents on Electronic Markets: Customization, Preference Revelation and Pricing.

Apart from reducing buyer search costs, web-based commerce has also enabled the use of intelligent agent technologies that reduce seller search costs by targeting buyers, customizing, and pricing products in real-time. Our model of an electronic market with customizable products analyzes the pricing, profitability and welfare implications of these agent-based technologies that price dynamically, based on product preference and demographic information revealed by consumers. We find that in making the trade-off between better prices and better customization, consumers invariably choose less-than-ideal products. Furthermore, this trade-off impacts buyers on the higher end of the market more, and causes a transfer of consumer surplus towards buyers with a lower willingness to pay. As buyers adjust their product choices in response to better demand agent technologies, sellers may experience reduced revenues, since the gains from better buyer information are countered by the lowering of the total value created from the transactions. We study the strategic and welfare implications of these findings, and discuss managerial and technology development guidelines.Information Systems Working Papers Serie

Open Source Infrastructure for Differentiable Density Functional Theory

Learning exchange correlation functionals, used in quantum chemistry calculations, from data has become increasingly important in recent years, but training such a functional requires sophisticated software infrastructure. For this reason, we build open source infrastructure to train neural exchange correlation functionals. We aim to standardize the processing pipeline by adapting state-of-the-art techniques from work done by multiple groups. We have open sourced the model in the DeepChem library to provide a platform for additional research on differentiable quantum chemistry methods

Intelligent agents in electronic markets for information goods: customization, preference revelation and pricing

Electronic commerce has enabled the use of intelligent agent technologies that can evaluate buyers, customize products, and price in real-time. Our model of an electronic market with customizable products analyzes the pricing, profitability and welfare implications of agent-based technologies that price dynamically based on product preference information revealed by consumers. We find that in making the trade-off between better prices and better customization, consumers invariably choose less-than-ideal products. Furthermore, this trade-off has a higher impact on buyers on the higher end of the market and causes a transfer of consumer surplus towards buyers with a lower willingness to pay. As buyers adjust their product choices in response to better demand agent technologies, seller revenues decrease since the gains from better buyer information are dominated by the lowering of the total value created from the transactions. We study the strategic and welfare implications of these findings, and discuss managerial and technology development guidelines.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

The Impact of Intelligent Agents on Electronic Markets: Customization, Preference Revelation and Pricing.

Apart from reducing buyer search costs, web-based commerce has also enabled the use of intelligent agent technologies that reduce seller search costs by targeting buyers, customizing, and pricing products in real-time. Our model of an electronic market with customizable products analyzes the pricing, profitability and welfare implications of these agent-based technologies that price dynamically, based on product preference and demographic information revealed by consumers. We find that in making the trade-off between better prices and better customization, consumers invariably choose less-than-ideal products. Furthermore, this trade-off impacts buyers on the higher end of the market more, and causes a transfer of consumer surplus towards buyers with a lower willingness to pay. As buyers adjust their product choices in response to better demand agent technologies, sellers may experience reduced revenues, since the gains from better buyer information are countered by the lowering of the total value created from the transactions. We study the strategic and welfare implications of these findings, and discuss managerial and technology development guidelines.Information Systems Working Papers Serie

Superscalar Processor Performance Enhancement through Reliable Dynamic Clock Frequency Tuning

Synchronous circuits are typically clocked considering worst case timing paths so that timing errors are avoided under all circumstances. In the case of a pipelined processor, this has special implications since the operating frequency of the entire pipeline is limited by the slowest stage. Our goal, in this paper, is to achieve higher performance in superscalar processors by dynamically varying the operating frequency during run time past worst case limits. The key objective is to see the effect of overclocking on superscalar processors for various benchmark applications, and analyze the associated overhead, in terms of extra hardware and error recovery penalty, when the clock frequency is adjusted dynamically. We tolerate timing errors occurring at speeds higher than what the circuit is designed to operate at by implementing an efficient error detection and recovery mechanism. We also study the limitations imposed by minimum path constraints on our technique. Experimental results show that an average performance gain up to 57% across all benchmark applications is achievable

Partially Observable Games for Secure Autonomy

Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade. In this paper, we report our ongoing effort to integrate these two presently distinct areas into a single framework. To this end, we propose the two-player partially observable stochastic game formalism to capture both high-level autonomous mission planning under uncertainty and adversarial decision making subject to imperfect information. We show that synthesizing sub-optimal strategies for such games is possible under finite-memory assumptions for both the autonomous decision maker and the cyber-adversary. We then describe an experimental testbed to evaluate the efficacy of the proposed framework

Partially Observable Games for Secure Autonomy

Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade. In this paper, we report our ongoing effort to integrate these two presently distinct areas into a single framework. To this end, we propose the two-player partially observable stochastic game formalism to capture both high-level autonomous mission planning under uncertainty and adversarial decision making subject to imperfect information. We show that synthesizing sub-optimal strategies for such games is possible under finite-memory assumptions for both the autonomous decision maker and the cyber-adversary. We then describe an experimental testbed to evaluate the efficacy of the proposed framework