Decoding human behavior with big data? Critical, constructive input from the decision sciences

Abstract

Big data analytics employs algorithms to uncover people's preferences and values, and support their decision making. A central assumption of big data analytics is that it can explain and predict human behavior. We investigate this assumption, aiming to enhance the knowledge basis for developing algorithmic standards in big data analytics. First, we argue that big data analytics is by design atheoretical and does not provide process-based explanations of human behavior; thus, it is unfit to support deliberation that is transparent and explainable. Second, we review evidence from interdisciplinary decision science, showing that the accuracy of complex algorithms used in big data analytics for predicting human behavior is not consistently higher than that of simple rules of thumb. Rather, it is lower in situations such as predicting election outcomes, criminal profiling, and granting bail. Big data algorithms can be considered as candidate models for explaining, predicting, and supporting human decision making when they match, in transparency and accuracy, simple, process-based, domain-grounded theories of human behavior. Big data analytics can be inspired by behavioral and cognitive theory.

Tools of digital innovation in public affairs management: A practice‐oriented analysis

“…AI algorithms can also “play” with these data formulating data‐driven expectations (the red and green forecasting in Figure 1), a feature that may be somehow useful (to provide more concrete evidence or technical support for specific strategic decisions), but that is subject to several limitations (Domahidi et al, 2019; Katsikopoulos & Canellas, 2022), starting with the epistemological problem of the fragility of past‐based inductions and of necessarily imperfect or biased algorithms.…”

“…The production and availability of massive amounts of data is a direct consequence of the progressive digitalization of the world, a process that is transferring most of our activities into the digital realm (a process that the Covid pandemic has accelerated and made most evident, but that has started long ago), with individual actions, communications and events (both human and non‐human) leaving a large digital trace of data and big data (Cukier & Mayer‐Schoenberger, 2013; Katsikopoulos & Canellas, 2022). This process of datafication provides the opportunity to achieve a better knowledge of the world, for governments, for researchers, but also for political, economic, and social players (Helles & Ørmen, 2020; Lazer et al, 2020; Leech, 2020; Lnenicka & Komarkova, 2019; Pentland, 2014).…”

Integrating Multisource Geographic Big Data to Delineate Urban Growth Boundary: A Case Study of Changsha

“…The emergence of geographic big data covering such topics as human behavior and earth observations provides highresolution, wide-coverage, real-time dynamic data for urban dynamic feature sensing [31]. Human behavior big data includes the tremendous volume of spatiotemporal behavioral information generated in people's daily lives including cell phone signals, Weibo check-ins, housing rents, traffic operations, and social media data [32], [33]. These data have the advantages of large sample size, high spatial and temporal resolution, expressive spatial differences, and descriptiveness of interactions, which can be used in research such as urban spatial expansion, resident behavioral characteristics, and separation of employment and housing [34], [35].…”

Bounded Rationality a Prelude to Ecological Rationality: A Deeper Look at the Fast and Frugal Heuristic in Leadership

“…The opposite of using statistical data and probability modeling to inform decisions is fast and frugal heuristics, a process involving using people's available information (Katsikopoulos & Canellas, 2022). The decision-making process is simple, faster, and feasible for humans and organizations (Lejarraga & Pindard-Lejarraga, 2020, p. 289).…”