An Integrated Human Decision Making Model under Extended Belief-Desire-Intention Framework

Author(s):  
Young-Jun Son
2014 ◽  
Vol 23 (06) ◽  
pp. 1460023 ◽  
Author(s):  
J. Sukarno Mertoguno

Real-time autonomy is a key element for system which closes the loop between observation, interpretation, planning, and action, commonly found in UxV, robotics, smart vehicle technologies, automated industrial machineries, and autonomic computing. Real-time autonomic cyber system requires timely and accurate decision making and adaptive planning. Autonomic decision making understands its own state and the perceived state of its environment. It is capable of anticipating changes and future states and projecting the effects of actions into future states. Understanding of current state and the knowledge/model of the world are needed for extrapolating actions and deriving action plans. This position paper proposes a hybrid, statistical-formal approach toward achieving realtime autonomy.


2019 ◽  
Author(s):  
Sophie Hilgard ◽  
Nir Rosenfeld ◽  
Mahzarin R. Banaji ◽  
Jack Cao ◽  
David Parkes

We propose a new, complementary approach to interpretability, in which machines are not considered as experts whose role it is to suggest what should be done and why, but rather as advisers. The objective of these models is to communicate to a human decision-maker not what to decide but how to decide. In this way, we propose that machine learning pipelines will be more readily adopted, since they allow a decision-maker to retain agency. Specifically, we develop a framework for learning representations by humans, for humans, in which we learn representations of inputs (‘advice’) that are effective for human decision-making. Representation generating models are trained with humans-in-the-loop, implicitly incorporating the human decision-making model. We show that optimizing for human decision-making rather than accuracy is effective in promoting good decisions in various classification tasks while inherently maintaining a sense of interpretability.


Author(s):  
Anthony Jang ◽  
Ravi Sharma ◽  
Jan Drugowitsch

AbstractTraditional accumulation-to-bound decision-making models assume that all choice options are processed simultaneously with equal attention. In real life decisions, however, humans tend to alternate their visual fixation between individual items in order to efficiently gather relevant information [46, 23, 21, 12, 15]. These fixations also causally affect one’s choices, biasing them toward the longer-fixated item [38, 2, 25]. We derive a normative decision-making model in which fixating a choice item boosts information about that item. In contrast to previous models [25, 39], we assume that attention enhances the reliability of information rather than its magnitude, consistent with neurophysiological findings [3, 13, 29, 45]. Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation patterns and fixation-related choice biases seen in human decision-makers, and provides a Bayesian computational rationale for the fixation bias. This insight led to additional behavioral predictions that we confirmed in human behavioral data. Finally, we explore the consequences of changing the relative allocation of cognitive resources to the attended versus the unattended item, and show that decision performance is benefited by a more balanced spread of cognitive resources.


2013 ◽  
Author(s):  
Scott D. Brown ◽  
Pete Cassey ◽  
Andrew Heathcote ◽  
Roger Ratcliff

2019 ◽  
pp. 125-133
Author(s):  
Duong Truong Thi Thuy ◽  
Anh Pham Thi Hoang

Banking has always played an important role in the economy because of its effects on individuals as well as on the economy. In the process of renovation and modernization of the country, the system of commercial banks has changed dramatically. Business models and services have become more diversified. Therefore, the performance of commercial banks is always attracting the attention of managers, supervisors, banks and customers. Bank ranking can be viewed as a multi-criteria decision model. This article uses the technique for order of preference by similarity to ideal solution (TOPSIS) method to rank some commercial banks in Vietnam.


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