Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions

A Decision Procedure for a Temporal Belief Logic

. This paper presents a temporal belief logic called L TB . In addition to the usual connectives of linear discrete temporal logic, L TB contains an indexed set of modal belief connectives, via which it is possible to represent the belief systems of resource-bounded reasoning agents. The applications of L TB in general, and its use for representing the dynamic properties of multi-agent AI systems in particular, are discussed in detail. A tableau-based decision procedure for L TB is then described, and some examples of its use are presented. The paper concludes with a discussion and future work proposals. 1 Introduction Temporal logics have been shown to have many applications, in a variety of disciplines. For example: in computer science, temporal logics are used in the specification and verification of reactive systems [16]; in artificial intelligence, they are used as knowledge representation formalisms, and have proved to be a valuable tool in tackling such problems as reasoning ab..