A decision network account of reasoning about other people’s choices

Highlights

• People can predict what others will choose and infer what others know and want.

• Decision networks help to explain how people make inferences like these.

• Decision networks extend Bayes nets by adding a notion of goal-directed choice.

• In four experiments, people’s inferences were best predicted by decision networks.

Abstract

The ability to predict and reason about other people’s choices is fundamental to social interaction. We propose that people reason about other people’s choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference.

Introduction

People tend to assume that other people’s behavior results from their conscious choices—for example, choices about what outfit to wear, what movie to watch, or how to respond to a question (Gilbert and Malone, 1995, Ross, 1977). Reasoning about choices like these requires an understanding of how they are motivated by mental states, such as what others know and want. Even though mental states are abstract entities and are inaccessible to other people, most people find it natural to make predictions about what others will choose and infer why they made the choices they did. In this paper, we explore the computational principles that support such inferences.

Several models of how people reason about others’ actions have been proposed by social and developmental psychologists (e.g., Gilbert, 1998, Jones and Davis, 1965, Malle and Knobe, 1997, Wellman and Bartsch, 1988). Four examples are shown in Fig. 1. For instance, Fig. 1a shows a model of how people reason about others’ actions. According to this model, a person’s dispositional characteristics combine to produce an intention to take an action. Then, if that person has the necessary knowledge and ability to carry out the action, he or she takes the action, producing a set of effects. The other three models in Fig. 1 take into account additional variables such as belief and motivation. For example, Fig. 1b proposes that people take actions that they believe will satisfy their desires. The models in Fig. 1 are not computational models, but they highlight the importance of structured causal representations for reasoning about choices and actions. We show how representations like these can serve as the foundation for a computational account of social reasoning.

Our account draws on two general themes from the psychological literature. First, people tend to assume that choices, unlike world events, are goal-directed (Baker et al., 2009, Csibra and Gergely, 1998, Csibra and Gergely, 2007, Goodman et al., 2009, Shafto et al., 2012). We refer to this assumption as the principle of goal-directed choice, although it has also been called the intentional stance (Dennett, 1987) and the principle of rational action (Csibra and Gergely, 1998, Csibra and Gergely, 2007). Second, we propose that human reasoning relies on probabilistic inference. Recent work on inductive reasoning has emphasized the idea that probabilistic inference can be carried out over structured causal representations (Griffiths and Tenenbaum, 2005, Griffiths et al., 2010, Tenenbaum et al., 2011), and our account relies on probabilistic inference over representations similar to those in Fig. 1.

Our account is related to previous work on Bayes nets (short for Bayesian networks; Pearl, 2000), which have been widely used to account for causal reasoning (Gopnik et al., 2004, Sloman, 2005). Bayes nets rely on probabilistic inference over structured causal representations, but they do not capture the principle of goal-directed choice. In this paper, we present an extension of Bayes nets called decision networks¹ (Howard and Matheson, 2005, Russell and Norvig, 2010) that naturally captures the principle of goal-directed choice. We propose that people reason about choice behavior by constructing mental models of other people’s choices that are similar to decision networks and then performing probabilistic inference over these mental models. Decision networks may therefore provide some computational substance to qualitative models like the ones in Fig. 1.

Our decision network account of reasoning about choices builds on previous approaches, including the theory theory of conceptual structure. The theory theory proposes that children learn and reason about the world by constructing scientific-like theories that are testable and subject to revision on the basis of evidence (Gopnik & Wellman, 1992). These theories can exist at different levels of abstraction. Framework theories capture fundamental principles that are expected to apply across an entire domain, and these framework theories provide a basis for constructing specific theories of concrete situations (see Wellman & Gelman, 1992). The decision network account can be viewed as a framework theory that captures the idea that choices are made in order to achieve goals, whereas individual decision networks can be viewed as specific theories. Gopnik and Wellman (2012) argue that Bayes nets provide a way to formalize the central ideas of the theory theory, and their reasons apply equally well to decision networks. For example, decision networks can be used to construct abstract causal representations of the world, to predict what will happen next, or to infer unobserved causes.

Although decision networks have not been previously explored as psychological models, they have been used by artificial intelligence researchers to create intelligent agents in multi-player games (Gal and Pfeffer, 2008, Koller and Milch, 2003, Suryadi and Gmytrasiewicz, 1999). In the psychological literature there are a number of computational accounts of reasoning about behavior (Bello and Cassimatis, 2006, Bonnefon, 2009, Bonnefon and Sloman, 2013, Hedden and Zhang, 2002, Oztop et al., 2005, Shultz, 1988, Van Overwalle, 2010, Wahl and Spada, 2000), and some of these accounts rely on Bayes nets (Goodman et al., 2006, Hagmayer and Osman, 2012, Hagmayer and Sloman, 2009, Sloman and Hagmayer, 2006, Sloman et al., 2012). However, our work is most closely related to accounts that extend the framework of Bayes nets to include the principle of goal-directed choice (Baker and Tenenbaum, 2014, Baker et al., 2008, Baker et al., 2009, Baker et al., 2011, Doshi et al., 2010, Goodman et al., 2009, Jara-Ettinger et al., 2012, Pantelis et al., 2014, Pynadath and Marsella, 2005, Tauber and Steyvers, 2011, Ullman et al., 2009). Much of this work uses a computational framework called Markov decision processes (MDPs; Baker and Tenenbaum, 2014, Baker et al., 2009).

In the next section, we describe the decision network framework in detail and explain how it is related to the Bayes net and MDP frameworks. We then present four experiments that test predictions of the decision network framework as an account of how people reason about choice behavior. Our first two experiments are specifically designed to highlight unique predictions of decision networks that distinguish them from an account based on standard Bayes nets. Our second two experiments focus on inferences about mental states. Experiment 3 focuses on inferences about what someone else knows and Experiment 4 focuses on inferences about what someone else’s goals are.