Benefits of Neuroeconomic Modeling: New Policy Interventions and Predictors of Preference

Abstract

Neuroeconomics aims at providing a detailed computational and neurobiological account of the decision-making process that will serve as a foundation for a better understanding of human behavior and well-being (Fehr and Rangel 2011). This means that data such as neural activity, response times (RT), and eye movements play an important role in this research program.However, much opposition to neuroeconomics has centered on the idea that standard economic theory makes no predictions about non-choice data. This stems from a long-held reluctance among economists to model the process of decision-making and to instead focus only on its outcome, namely the resulting choice. To address this, recent neuroeconomic models have sought to demonstrate the usefulness of jointly relating choice frequencies and process measures to underlying latent valuations. Here we show, in particular, that (i) neuroeconomic driftdiffusion models (DDMs) yield new -empirically validated -insights into the potential suboptimalities of individual decision-making that can be mitigated with novel policy interventions, and (ii) that the time it takes to make decisions provides an informative signal about people's preferences. Thus, additional measures related to the choice process can be useful for both positive and normative economics. Overall, we use the DDM to make a general point that there is substantial value to jointly modeling choices and other process measures, even if a researcher is only concerned with choices.The problem that the DDM addresses is that of stochastic choice. Inconsistent choice behavior is well documented and necessitates the use of probabilistic approaches such as logistic choice models (Luce 1959), random utility theory (McFadden 1973), and Quantal Response Equilibria (McKelvey and Palfrey 1995). Some have argued that such probabilistic behavior is still utilitymaximizing behavior and that it only appears noisy due to unobserved characteristics of the

Stochastic Choice: An Optimizing Neuroeconomic Model

“…8 As discussed in Woodford (2014), the model's predictions are nearly independent of the value of N , for any large enough value of N , once the numerical values of ρ and ∆ are suitably adjusted. 9 No allowance is made in this case for a fixed time requirement A in addition to the time T ∆ used by the algorithm, so that again three free parameters are used to fit the data.…”

Quantum Model of Decision-Making in Economics

“…In addition more recently, the economic decision-making models are under pressure from neuroscience. Krajbich et al [3] questions the workhorse model in economics because it does not fit empirical decision-making data [2] [17]. A new promising approach is the drift-diffusion model…”

“…According to recent neuroscientific research by Krajbich et al [3], Baumgartner [4] and Yukalov and Sornette [24], the model requires a brain specific threshold that I define similarly by the quarter law. In other words, the average absolute value of the attraction factor is estimated by 1 The ordering procedure is discussed in [18].…”

“…For instance, Krajbich et al [3] explains that "(...) options that receive more attention also receive more evidence (...)". Consequently, the QMDM holds great promise as a human decision-making model with preferences over risk, time, and social interaction, in the future.…”

“…The model approach is based on recent neuroeconomic evidence [1]- [3]. This means that data such as neural activity is used to design a suitable alternative to the standard decision-making theory in economics [4].…”