Experts and novices differ with respect to the use of intuition and deliberation in their decision-making processes, which affects the quality of decisions they make. We often ask ourselves: where does expert intuition and deliberation come from? If the progression from novice to expert is made through learning experiences, what should we provide novices during training? Identifying the discrepancies between experts and novices is essential for developing a computational learning system that simulates the human decision-making process. In this paper, we investigate the difference between individuals operating Unmanned Aerial Vehicle (UAV) missions, collected in a dataset called the Supervisory Control Operations User Test bed (SCOUT), by analyzing their computational models. For the computational models, deep neural networks (DNNs) and double transition models (DTMs) were employed. A set of DNNs was constructed from biometric information about eye movements, and a set of DTMs was built from event-driven data associated with actions taken by the individuals. For investigating DNNs, we examined how much improvement was obtained during training and validation, while for DTMs, we concentrated on the reward distributions of trajectories derived through inverse reinforcement learning (IRL). We classified SCOUT subjects into three levels of expertise according to their selfassessment and the maximum score achieved: novice, intermediate and expert. By analyzing these models, we identified differences between the expert and novice groups. In particular, the accuracy of the expert DNNs improved more effectively than that of novice DNNs, and the reward distributions of the expert DTMs were more closely clustered than those of novice DTMs.
A Commander’s decision making style represents how he weighs his choices and evaluates possible solutions with regards to his goals. Specifically, in the naval warfare domain, it relates the way he processes a large amount of information in dynamic, uncertain environments, allocates resources, and chooses appropriate actions to pursue. In this paper, we describe an approach to capture a Commander’s decision style by creating a cognitive model that captures his decisionmaking process and evaluate this model using a set of scenarios using an online naval warfare simulation game. In this model, we use the Commander’s past behaviors and generalize Commander's actions across multiple problems and multiple decision making sequences in order to recommend actions to a Commander in a manner that he may have taken. Our approach builds upon the Double Transition Model to represent the Commander's focus and beliefs to estimate his cognitive state. Each cognitive state reflects a stage in a Commander’s decision making process, each action reflects the tasks that he has taken to move himself closer to a final decision, and the reward reflects how close he is to achieving his goal. We then use inverse reinforcement learning to compute a reward for each of the Commander's actions. These rewards and cognitive states are used to compare between different styles of decision making. We construct a set of scenarios in the game where rational, intuitive and spontaneous decision making styles will be evaluated.
Modeling real-world scenarios is a challenge for traditional social science researchers, as it is often hard to capture the intricacies and dynamisms of real-world situations without making simplistic assumptions. This imposes severe limitations on the capabilities of such models and frameworks. Complex population dynamics during natural disasters such as pandemics is an area where computational social science can provide useful insights and explanations. In this paper, we employ a novel intent-driven modeling paradigm for such real-world scenarios by causally mapping beliefs, goals, and actions of individuals and groups to overall behavior using a probabilistic representation called Bayesian Knowledge Bases (BKBs). To validate our framework we examine emergent behavior occurring near a national border during pandemics, specifically the 2009 H1N1 pandemic in Mexico. The novelty of the work in this paper lies in representing the dynamism at multiple scales by including both coarse-grained (events at the national level) and finegrained (events at two separate border locations) information. This is especially useful for analysts in disaster management and first responder organizations who need to be able to understand both macro-level behavior and changes in the immediate vicinity, to help with planning, prevention, and mitigation. We demonstrate the capabilities of our framework in uncovering previously hidden connections and explanations by comparing independent models of the border locations with their fused model to identify emergent behaviors not found in either independent location models nor in a simple linear combination of those models.
KEYWORDS: Systems modeling, Process modeling, Social networks, Performance modeling, Network centric warfare, Bridges, Social network analysis, Telecommunications, Computer simulations, Data modeling
The major focus in the field of modeling & simulation for network centric environments has been on the physical layer
while making simplifications for the human-in-the-loop. However, the human element has a big impact on the
capabilities of network centric systems. Taking into account the socio-behavioral aspects of processes such as team
building, group decision-making, etc. are critical to realistically modeling and analyzing system performance. Modeling
socio-cultural processes is a challenge because of the complexity of the networks, dynamism in the physical and social
layers, feedback loops and uncertainty in the modeling data. We propose an overarching framework to represent, model
and analyze various socio-cultural processes within network centric environments. The key innovation in our
methodology is to simultaneously model the dynamism in both the physical and social layers while providing functional
mappings between them. We represent socio-cultural information such as friendships, professional relationships and
temperament by leveraging the Culturally Infused Social Network (CISN) framework. The notion of intent is used to
relate the underlying socio-cultural factors to observed behavior. We will model intent using Bayesian Knowledge Bases
(BKBs), a probabilistic reasoning network, which can represent incomplete and uncertain socio-cultural information. We
will leverage previous work on a network performance modeling framework called Network-Centric Operations
Performance and Prediction (N-COPP) to incorporate dynamism in various aspects of the physical layer such as node
mobility, transmission parameters, etc. We validate our framework by simulating a suitable scenario, incorporating
relevant factors and providing analyses of the results.
When compared to biological experiments, using computational protein models can save time and effort in identifying
native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identifying the native
conformation remains a computationally hard problem - even in simplified models such as hydrophobic-hydrophilic
(HP) models. Distributed systems have become the focus of protein folding, providing high performance computing
power to accommodate the conformation space. To use a distributed system efficiently (with limited resources), an
appropriate strategy should be designed accordingly. Communication incurs overhead but can provide useful
information in distributed systems through careful consideration. Our study focuses on understanding the behavior of
distributed systems and developing an efficient communication strategy to save computational effort in order to obtain
good solutions. In this paper, we propose a distributed caching strategy, which reuses partial results of computations and
transmits the cached and reusable information among neighboring inter-connected processors. In order to validate this
idea in a practical setting, we present algorithms to retrieve and restore the cached information and apply them to 2D
triangular HP lattice models through coarse-grained parallel genetic algorithms (CPGAs). Our experimental results
demonstrate the time savings as well as the limits in caching improvements for our distributed caching strategy.
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