An integrative formal model of motivation and decision making: The MGPM*.

Abstract

We develop and test an integrative formal model of motivation and decision making. The model, referred to as the extended multiple-goal pursuit model (MGPM*), is an integration of the multiple-goal pursuit model (Vancouver, Weinhardt, & Schmidt, 2010) and decision field theory (Busemeyer & Townsend, 1993). Simulations of the model generated predictions regarding the effects of goal type (approach vs. avoidance), risk, and time sensitivity on prioritization. We tested these predictions in an experiment in which participants pursued different combinations of approach and avoidance goals under different levels of risk. The empirical results were consistent with the predictions of the MGPM*. Specifically, participants pursuing 1 approach and 1 avoidance goal shifted priority from the approach to the avoidance goal over time. Among participants pursuing 2 approach goals, those with low time sensitivity prioritized the goal with the larger discrepancy, whereas those with high time sensitivity prioritized the goal with the smaller discrepancy. Participants pursuing 2 avoidance goals generally prioritized the goal with the smaller discrepancy. Finally, all of these effects became weaker as the level of risk increased. We used quantitative model comparison to show that the MGPM* explained the data better than the original multiple-goal pursuit model, and that the major extensions from the original model were justified. The MGPM* represents a step forward in the development of a general theory of decision making during multiple-goal pursuit. (PsycINFO Database Record

Understanding the motivations of patients: A co‐designed project to understand the factors behind patient engagement

“…Recognizing that people often pursue multiple, competing goals, they not only strive to achieve the desired outcomes but also attempt to maximize the value they receive. 34 Since individuals have limited time and resources, they often have to choose what choice to prioritize. Simply speaking, individuals initially form a 'consideration set' consisting of 'alternatives from which choice is made', 35 , p. 522.…”

A General Architecture for Modeling the Dynamics of Goal-Directed Motivation and Decision-Making

“…Unlike the goal gradient hypothesis, control theory does not explicitly address avoidance goals. Some have argued on the basis of control theory that, when faced with an avoidance goal, individuals are motivated to enlarge the discrepancy (e.g., Ballard, Yeo, Loft, et al, 2016;Carver & Scheier, 1998). This argument suggests that motivation to avoid should decrease with increasing distance to the undesired state, which is the same prediction the goal gradient hypothesis makes regarding the avoidance context.…”

“…More recently, the discrepancy perspective has been successful in explaining complex resource allocation process undertaken when pursuing multiple goals. In this context, people often direct their time and effort toward the goal with the largest discrepancy (Ballard, Yeo, Loft, Vancouver, & Neal, 2016;Schmidt & DeShon, 2007;Schmidt, Dolis, & Tolli, 2009). It should be noted that this work has almost exclusively addressed approach goals.…”

An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data

“…Step 1: Simulating the Model The first step in evaluating the model is to conduct a simulation study. A simulation study involves fixing the model parameters to predefined values and examining the output that emerges from the model under those conditions (Ballard et al, 2016;Ballard, Yeo, Vancouver, & Neal, 2017;Grand et al, 2016;Vancouver et al, 2018). Simulation is an important first step in evaluating a computational model for several reasons.…”