Agricultural decision making and climate uncertainty in developing countries

Abstract

In situations of uncertainty, people often make decisions with heuristic shortcuts or decision rules, rather than using computational or logical methods such as optimizing their behavior based on specific goals. The high level of uncertainty and complexity involved in adapting to climate change suggests that heuristics would be commonly used in this context rather than more structured decision methods. Through a systematic review of 137 articles, from 2007–2017 we explore the behavioral and cognitive assumptions used to examine agricultural decision-making related to climate change among farmers in developing countries. We find a strong orientation toward modeling behavior and decision making as a rational utility-maximizing process, despite decades of research demonstrating the prevalence of simpler heuristic choice when facing uncertainty and real-world constraints. Behavioral and cognitive approaches can increase our ability to predict or explain decisions being made in this realm, particularly in terms of how we understand decision making around information processing and risk assessment. In the following review, we highlight articles that have contributed to developing a more realistic decision-making framework for studying this problem on the ground. While there is a burgeoning literature using psychological insights to examine decision making under climate uncertainty, few studies consider the prevalence of simple heuristics, the presence of cognitive biases, and the salience of climate relative to other risk factors.

Integrating Behavioral Theories in Agent-Based Models for Agricultural Drought Risk Assessments

“…People are often not able to analyze all the available information to come to the optimal decision, instead they use simple decision rules (heuristics) based on limited information, which can lead to biases in their decisions (Tversky and Kahneman, 1974). Climate change and its impact on droughts is characterized with uncertainty and the availability of reliable climate information is often limited especially for smallholder farmers in low-and middle-income countries, which makes it likely that those farmers to some degree base their drought adaptation decision on simple heuristics (Waldman et al, 2020). Decision rules in an ABM can be solely based on simple heuristics (Deadman et al, 2004;Dobbie and Balbi, 2017).…”

Measuring Disaster Crop Production Losses using Survey Microdata: Evidence from Sub-Saharan Africa

“…2) They provide high data density. Relative to site-based models, this reduces the need for strong assumptions about the generalizability of the sample for larger aggregates (though it may not eliminate it entirely) (van Wart et al, 2013;van Bussel et al, 2015).…”

“…Lastly, a number of factors commonly studied in the behavioral literature may matter for the measurement of crop production losses through survey data (Schilbach, Schofield and Mullainathan, 2016;Waldman et al, 2020). Examples include availability bias 7 (Tversky and Kahneman, 1973;Karlan et al, 2014;Brown et al, 2018), scarcity 8 (Shah, Mullainathan and Shafir, 2012;Mani et al, 2013;Lichand and Mani, 2020), satisficing 9 (Krosnick and Presser, 2010), and a large body of methodological research on the accuracy of farmers' self-reports (Carletto, Savastano and Zezza, 2013;Carletto, Gourlay and Winters, 2015;Arthi et al, 2018;Gourlay, Kilic and Lobell, 2019;Wollburg, Tiberti and Zezza, 2021).…”

Rules of thumb and cultural evolution: How simple heuristics have guided human adaptation and the emergence of cultural complexity

“…Critics have argued that many prevalent cognitive models and computational models on human behaviour rely on unrealistically complex cognitive decision-making processes (Gigerenzer, 2008b;Waldman et al, 2020). For instance, many models assume some sort of optimization or utility maximisation as a starting point, yet when looking at human behaviour it is clear that in most cases such optimization is not possible or even an option (this applies even when humans "optimize under constraints"; see Gigerenzer, 2008b).…”