Knowledge-based Modeling in Dynamic Decision Making

Knowledge-based Modeling in Dynamic Decision Making Angel Iglesias (angel.iglesias@csic.es) Bioengineering Group, Spanish National Research Council (CSIC) Arganda del Rey, 28500-Madrid, Spain M. Dolores Del Castillo (lola@iai.csic.es) Bioengineering Group, Spanish National Research Council (CSIC) Arganda del Rey, 28500-Madrid, Spain J. Ignacio Serrano (jignacio.serrano@csic.es) Bioengineering Group, Spanish National Research Council (CSIC) Arganda del Rey, 28500-Madrid, Spain Jesus Oliva (jesus.oliva@csic.es) Bioengineering Group, Spanish National Research Council (CSIC) Arganda del Rey, 28500-Madrid, Spain Abstract This seemingly simple stock problem is actually unintu- itive and difficult. In fact, there is evidence that suggests that people do not perceive stocks and flows dynamics correctly. For example, in an experiment where graduate students were asked to sketch the evolution of the water level in a bathtub over time (given simple patterns for the environmental inflow and outflow), only 36% of the students answered correctly (Sterman, 2002). A knowledge-based model that emulates human behavior in a Dynamic Decision Making task is proposed. The model, MAIDEN-DSF, uses a connectionist representation of knowl- edge and a value function to compute the best alternative. In order to validate MAIDEN-DSF, two data sets have been used: a training set and a test set that contain the behavior of partic- ipants that performed the task with different conditions. The results suggest that MAIDEN-DSF is a considerable frame- work in order to model human behavior. The aim of this paper is to use MAIDEN-DSF to prove that participants do not per- ceive delay conditions when dealing with Dynamic Decision Making tasks. Keywords: Decision Making; Computational Models; Con- nectionism. Introduction Dynamic Decision Making lies in tasks that require a se- ries of decisions where the state of the world changes, both autonomously and as a consequence of the decisions made (Brehmer, 1992). The Dynamic Stocks and Flows (DSF) task (Gonzalez & Dutt, 2011), emulates such situations with three elements: a single stock represented by a water tank; inflows, which increase the level of the stock; and outflows, which de- crease the level of the stock. The goal of this task is to keep the stock at a certain level over 100 time periods. In every time period, a participant can control the stock adding or re- moving water via two inputs that represent the decision: the user inflow (UI) and the user outflow (UO) values. Besides, there are two external inputs that represent the environment inflow (EI) and outflow (EO) values, both of them are not directly controlled by the participant. The dynamics of the environment is unknown to the participant, so the EI and EO values have to be predicted using the experience acquired in previous time periods. When the participant makes a deci- sion, the DSF determines the level of water in the tank by adding the UI and EI to the level in the tank and subtract- ing the UO and the EO. Then the DSF presents the resulting level, the goal level and the last EI, EO, UI and UO values within the following time period. The DSF has been studied under different conditions (Cronin & Gonzalez, 2007) and there is evidence that the hardest condition for controlling the water tank is when the user inputs are delayed for three time periods (Lebiere, Gon- zalez, & Warwick, 2010). Participants have some understand- ing about the dynamics of the tank, for example, if 1 gallon of water is added, then the level is increased 1 gallon. How- ever, with this delay, participants seem to have incorrect be- liefs about the relationship between stocks and delayed flows or participants do not perceive the delay and, therefore, they behave as if there was no delay. The aim of this paper is to use a knowledge-based model that emulates human behavior in the DSF in order to find cues that prove that participants do not actually perceive the delay. The proposed model is based on MAIDEN, a Model of As- sessment and Inference of DEcisions based on a Net of con- cepts (Iglesias, Del Castillo, Serrano, & Oliva, 2010). A knowledge-based model MAIDEN-DSF, the implementation of MAIDEN for dealing with DSF, is divided in two main phases. The first phase lies in the estimation of EI and EO values using a connectionist representation of knowledge called decision net. In the sec- ond phase, MAIDEN-DSF uses a value function to choose the best alternative, which will be the one that sets the amount in the tank at the goal level corresponding with the estimated EI and EO values of the first phase.