Learning Prescriptive ReLU Networks

We study the problem of learning optimal policy from a set of discrete treatment options using observational data. We propose a piecewise linear neural network model that can balance strong prescriptive performance and interpretability, which we refer to as the prescriptive ReLU network, or P-ReLU. We show analytically that this model (i) partitions the input space into disjoint polyhedra, where all instances that belong to the same partition receive the same treatment, and (ii) can be converted into an equivalent prescriptive tree with hyperplane splits for interpretability. We demonstrate the flexibility of the P-ReLU network as constraints can be easily incorporated with minor modifications to the architecture. Through experiments, we validate the superior prescriptive accuracy of P-ReLU against competing benchmarks. Lastly, we present examples of interpretable prescriptive trees extracted from trained P-ReLUs using a real-world dataset, for both the unconstrained and constrained scenarios.Comment: 17 pages, 6 figures, accepted at ICML 2

Exploring foundations for using simulations in IS research

Simulation has been adopted in many disciplines as a means for understanding the behavior of a system by imitating it through an artificial object that exhibits a nearly identical behavior. Although simulation approaches have been widely adopted for theory building in disciplines such as engineering, computer science, management, and social sciences, their potential in the IS field is often overlooked. The aim of this paper is to understand how different simulation approaches are used in IS research, thereby providing insights and methodological recommendations for future studies. A literature review of simulation studies published in top-tier IS journals leads to the definition of three classes of simulations, namely the self-organizing, the elementary, and the situated. A set of stylized facts is identified for characterizing the ways in which the premise, the inference, and the contribution are presented in IS simulation studies. As a result, this study provides guidance to future simulation researchers in designing and presenting findings

Comparing Formal and Informal Institutions with the Institutional Grammar Tool

Conference Paper"While the role of formal and informal institutions has been long recognized among common-pool resources scholars working under the Institutional Analysis and Development Framework (IAD), not much attention has been devoted to disentangling the relative influence of each one on social behavior. We explore this issue through the application of the grammar of institutions, semi-structured interviews, and Q-sort methods. The goal of this paper is two-fold. First, the paper seeks to provide a deeper understanding of the interplay between formal and informal institutions on policy compliance. We do so in the context of aquaculture policies in the State of Colorado, USA. Second, this paper seeks to continue to develop Crawford and Ostrom’s grammar of institutions as an analytical tool for systematic institutional analysis. The results from the case study are mixed. We found some respondents reporting strong alignment between informal and the formal institutions but others reporting weak alignment. Additionally, feelings of personal guilt or shame and fear of social disapproval, together, were cited as being more influential in shaping individuals’ decision making regarding compliance with formal institutions than was fear of monetary sanctioning. The paper concludes with a discussion of the unexpected relationships among different syntactic elements of the grammar thereby deepening the understanding of how the grammar of institutions can help in the examination of policy documents and explain human behavior.

MACHINE LEARNING AND CAUSALITY FOR INTERPRETABLE AND AUTOMATED DECISION MAKING

This abstract explores two key areas in decision science: automated and interpretable decision making. In the first part, we address challenges related to sparse user interaction data and high item turnover rates in recommender systems. We introduce a novel algorithm called Multi-View Interactive Collaborative Filtering (MV-ICTR) that integrates user-item ratings and contextual information, improving performance, particularly for cold-start scenarios. In the second part, we focus on Student Prescription Trees (SPTs), which are interpretable decision trees. These trees use a black box teacher model to predict counterfactuals based on observed covariates. We experiment with a Bayesian hierarchical binomial regression model as the teacher and employ statistical significance testing to control tree growth, ensuring interpretable decision trees. Overall, our research advances the field of decision science by addressing challenges in automated and interpretable decision making, offering solutions for improved performance and interpretability

The Nature of Theory in Information Systems

The aim of this research essay is to examine the structural nature of theory in Information Systems. Despite the importance of theory, questions relating to its form and structure are neglected in comparison with questions relating to epistemology. The essay addresses issues of causality, explanation, prediction, and generalization that underlie an understanding of theory. A taxonomy is proposed that classifies information systems theories with respect to the manner in which four central goals are addressed: analysis, explanation, prediction, and prescription. Five interrelated types of theory are distinguished: (1) theory for analyzing, (2) theory for explaining, (3) theory for predicting, (4) theory for explaining and predicting, and (5) theory for design and action. Examples illustrate the nature of each theory type. The applicability of the taxonomy is demonstrated by classifying a sample of journal articles. The paper contributes by showing that multiple views of theory exist and by exposing the assumptions underlying different viewpoints. In addition, it is suggested that the type of theory under development can influence the choice of an epistemological approach. Support is given for the legitimacy and value of each theory type. The building of integrated bodies of theory that encompass all theory types is advocated