Studying mental models has recently received more attention, aiming to understand the cognitive aspects of human-computer interaction. However, there is not enough research on the elicitation of mental models in complex dynamic systems. We present Thought Bubbles as an approach for eliciting mental models and an avenue for understanding players’ mental model development in interactive virtual environments. We demonstrate the use of Thought Bubbles in two experimental studies involving 250 participants playing a supply chain game. In our analyses, we rely on Situation Awareness (SA) levels, including perception, comprehension, and projection, and show how experimental manipulations such as disruptions and information sharing shape players’ mental models and drive their decisions depending on their behavioral profile. Our results provide evidence for the use of thought bubbles in uncovering cognitive aspects of behavior by indicating how disruption location and availability of information affect people’s mental model development and influence their decisions.

Understanding decision-making in dynamic and complex settings is a challenge yet essential for preventing, mitigating, and responding to adverse events (e.g., disasters, financial crises). Simulation games have shown promise to advance our understanding of decision-making in such settings. However, an open question remains on how we extract useful information from these games. We contribute an approach to model human-simulation interaction by leveraging existing methods to characterize: (1) system states of dynamic simulation environments (with Principal Component Analysis), (2) behavioral responses from human interaction with simulation (with Hidden Markov Models), and (3) behavioral responses across system states (with Sequence Analysis). We demonstrate this approach with our game simulating drug shortages in a supply chain context. Results from our experimental study with 135 participants show different player types (hoarders, reactors, followers), how behavior changes in different system states, and how sharing information impacts behavior. We discuss how our findings challenge existing literature.

Optimizing operations on critical infrastructure networks is key to reducing the impact of disruptive events. In this paper, we explore the potential of having humans and algorithms work together to address this difficult task. For this purpose, we use a gamified experiment to build and assess this potential in the context of the debris collection problem (i.e., “gamesploring”). We developed a digital game where players can request the help of the computer while facing a multi-objective problem of assigning contractors to road segments for clearing debris in a disaster area. Through a within-subjects experimental study, we assessed how players optimized under various circumstances (e.g., initial solution vs. from scratch) compared to the computer on its own. The results are both surprising as well as insightful: they suggest that human-machine collaboration is indeed beneficial but also that more work is needed on how to appropriately guide this form of collaboration.

Agent-based simulations are widely used for modeling human behavior in various contexts. However, such simulations may oversimplify human decision-making. We propose the use of Gamettes to extract rich data on human decision-making and help in improving the human behavioral aspects of models underlying agent-based simulations. We show how Gamettes are designed and provide empirical validation for using Gamettes in an experimental supply chain setting to study human decision-making. Our results show that Gamettes are successful in capturing the expected behaviors and patterns in supply chain decisions, and, thus, we find evidence for the capability of Gamettes to inform behavioral models.

This paper presents a new framework for multimedia streaming that integrates the application and network layer functionalities to meet such stringent application requirements as delay and loss. The coordination between these two layers provides more robust media transmission even under severe network conditions. In this framework, a multiple description source coder is used to produce multiple independently-decodable streams that are routed over partially link-disjoint (non-shared) path to combat bursty packet losses. We model multi-path streaming and propose a multi-path streaming and propose a multi-path selection method that chooses a set of paths maximizing the overall quality at the client. Overlay infrastructure is then used to achieve multi-path routing over these selected paths. The simulation results show that the average peak signal-to-noise ratio (PSNR) improves by up to 8.1 dB, if the same source video is routed over intelligently selected multiple paths instead of the shortest path or maximally link-disjoint paths. In addition to PSNR improvement in quality, the end-user experiences a more continual steaming quality.

In a previous work [A. C. Begen, et al., 2003], we proposed an optimal multi-path selection method for multiple description (MD) encoded video streaming. To do so, we first modelled multi-path streaming and then developed an expression, i.e., an objective (cost) function, that estimated average streaming distortion in terms of network statistics, media characteristics and application requirements. Naturally, the ultimate goal was to find the set of paths that minimized this cost function. However, finding such sets of paths turned out to be intractable in large topologies. Hence, in this paper, we provide a fast heuristics-based solution by exploiting the infrastructure features of the Internet. The simulations run over various random Internet topologies show that the proposed heuristic is able to find a good solution in a much shorter time than the brute-force approach. Particularly, this heuristic is best suited to such interactive multimedia applications as video-conferencing and VoIP, where multi-path computation is a time-critical process. In addition, it is also suitable for the clients whose processing power capabilities are limited.

Multiple description (MD) and layered coding (LC) are two source-coding approaches proposed for adaptive and robust video streaming over lossy networks. Both streaming methods aim to improve the streaming quality by transmitting the generated multiple sub-bitstreams over partially link-disjoint paths. However, the achieved qualities heavily depend on the media characteristics, path conditions and application requirements. In order to attain the highest quality, we study optimal multi-path selection methods for both MD and LC streaming. The simulations run over an overlay infrastructure show 9.0 - 12.5 dB peak signal-to-noise ratio (PSNR) improvement when the video is streamed over intelligently selected multiple paths instead of the shortest path or maximally link-disjoint paths. By the help of the proposed path selection methods, the end users estimate the expected qualities of MD and LC streaming for the given network conditions and application requirements prior to the streaming, which allows the users to make a choice accordingly.