People have difficulties inferring the behavior of a stock variable from its inflows and outflows. Our goal is to offer low‐cost interventions to help overcome this difficulty. We hypothesize that the failure to understand accumulation relates to the cognitive mode of thinking: if people use their System 2 mode of thinking (analytical thinking), they are more likely to answer stock–flow questions correctly. We conduct an experiment with 400 participants and test effects of two interventions. The study replicates previously observed stock–flow failure and uncovers several variables that can influence subjects' response to the department store task. One particular finding is that having participants answer an analytical question right before the department store task marginally increases their chances of answering stock–flow questions correctly. Copyright © 2015 System Dynamics Society

We conduct textual analysis of a sample of more than 200,000 papers written on HIV/AIDS during the past three decades. Using the Latent Dirichlet Allocation method, we disentangle studies that address behavioral and social aspects from other studies and measure the trends of different topics as related to HIV/AIDS. We show that there is a regional variation in scientists’ approach to the problem of HIV/AIDS. Our results show that controlling for the economy, proximity to the HIV/AIDS problem correlates with the extent to which scientists look at the behavioral and social aspects of the disease rather than biomedical.

Recent evidence suggests that using the analytic mode of thinking (System 2) can improve people's performance in stock–flow (SF) tasks. In this paper, we further investigate the effects by implementing several different interventions in two studies. First, we replicate a previous finding that answering analytical questions before the SF task approximately doubles the likelihood of answering the stock questions correctly. We also investigate effects of three other interventions that can potentially prime participants to use their System 2. Specifically, the first group is asked to justify their response to the SF task; the second group is warned about the difficulty of the SF task; and the third group is offered information about cognitive biases and the role of the analytic mode of thinking. We find that the second group showed a statistically significant improvement in their performance. We claim that there are simple interventions that can modestly improve people's response in SF tasks. Copyright © 2017 System Dynamics Society

Analytical period-m motions and bifurcation trees in a periodically forced, van der Pol-Duffing oscillator are obtained through the Fourier series, and the corresponding stability and bifurcation of such period-m motions are discussed. To verify the approximate, analytical solutions of period-m motions on the bifurcation trees, numerical simulations are carried out, and the numerical results are compared with analytical solutions. The harmonic amplitude distributions are presented to show the significance of harmonic terms in the finite Fourier series of the analytical periodic solutions. The bifurcation trees of period-m motion to chaos via period-doubling are individually embedded in the quasiperiodic and chaotic motions without period-doubling.

John Morelock is a doctoral candidate at Virginia Tech. His research interests include student motivation, game-based learning, and gamified classrooms. He received the NSF Graduate Research Fellowship to study how engineering instructors use digital games, and how these teaching methods affect student motivation. In undergraduate engineering education, students are often overexposed to problem-solving methods that are unrepresentative of how problems are solved in engineering practice. For decision-making problems in particular, students are commonly taught to compare alternative solutions using information that is known and provided. However, many real-world decisionmaking problems require a broader range of problem-solving strategies, including information seeking, extrapolation of a decision's consequences, and compromise between parties with competing objectives. Accordingly, this paper describes a simulation game activity designed to offer industrial engineering seniors experience in solving realistic decision-making problems. The simulation game involved students working in teams that role-played as different types of companies in a global smartphone market, where teams needed to negotiate with one another to establish profitable contracts within the game's ruleset. In accordance with our learning objectives, we qualitatively examined how students sought information, adapted to changing conditions, and made decisions informed by constraints. Particularly, we sought to identify learning frameworks that fit the data well and would help us improve the design and assessment of the activity in later iterations. We found that the learning frameworks of metacognition and discrepancy resolution combined to explain most student activity relative to our learning objectives, and these frameworks suggest several points of improvement for the design and assessment of the simulation game.