Decision-making on process risk of Arctic route for LNG carrier via dynamic Bayesian network modeling

Dynamic Bayesian Decision - Making :

Computational modeling of dynamic decision making using connectionist networks

In this research connectionist modeling of decision making has been presented. Important areas for decision making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional decision making. In these kind of decisio...

Use of Bayesian Network to Support Risk-Based Analysis of LNG Carrier Loading Operation

This paper presents a methodology for risk analysis of LNG carriers operations aiming at defining the most critical pieces of equipments as for avoiding LNG leakage during loading and unloading operations. The pieces of equipment considered critical for loading and unloading operations are identified and the Cause-Consequence diagram is built. The probability of occurrence of each event listed ...

LNG decision making approaches compared.

Hazard zones associated with LNG handling activities have been a major point of contention in recent terminal development applications. Debate has reflected primarily worst case scenarios and discussion of these. This paper presents results from a maximum credible event approach. A comparison of results from several models either run by the authors or reported in the literature is presented. Wh...

Dynamic Non-Bayesian Decision Making

The model of a non-Bayesian agent who faces a repeated game with incomplete information against Nature is an appropriate tool for modeling general agent-environment interactions. In such a model the environment state (controlled by Nature) may change arbitrarily, and the feedback/reward function is initially unknown. The agent is not Bayesian, that is he does not form a prior probability neithe...