Models of Subjective Learning

We study a decision maker who faces a dynamic decision problem in which the process of information arrival is subjective. By studying preferences over menus of acts, we derive a sequence of utility representations that captures the decision maker’s uncertainty about the beliefs he will hold when choosing from a menu. In the most general model of second-order beliefs, we characterize a notion of "more preference for flexibility" via a subjective analogue of Blackwell’s (1951, 1953) comparisons of experiments. We proceed to analyze a model in which signals are subsets of the state space. The corresponding representation enables us to compare the behavior of two decision makers who expect to learn differently, even if they do not agree on their prior beliefs. The class of information systems that can support such a representation generalizes the notion of modeling information as a partition of the state space. We apply the model to study a decision maker who anticipates subjective uncertainty to be resolved gradually over time. We derive a representation that uniquely identifies both the filtration, which is the timing of information arrival with the sequence of partitions it induces, and the decision maker’s prior beliefs.Resolution of uncertainty, second-order beliefs, preference for flexibility, valuing binary bets more, generalized partition.

Modeling good research practices - overview: a report of the ISPOR-SMDM modeling good research practices task force - 1.

Models—mathematical frameworks that facilitate estimation of the consequences of health care decisions—have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR modeling task force reported in 2003 has led to a new task force, jointly convened with the Society for Medical Decision Making, and this series of seven papers presents the updated recommendations for best practices in conceptualizing models; implementing state–transition approaches, discrete event simulations, or dynamic transmission models; dealing with uncertainty; and validating and reporting models transparently. This overview introduces the work of the task force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these papers includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making

Are Fishermen Rational? A Fishing Expedition

Uncertainty is a defining characteristic of fisheries. Fishermen make decisions affecting their livelihood daily and even hourly, often with scant information on which to evaluate alternatives. Cognitive psychologists and behavioral economists have shown that decisions involving uncertainty often diverge substantially from what would be predicted by expected utility theory. I review relevant findings from the literature on decision making under uncertainty and previous empirical modeling of fishing decisions, and explore the implications of a number of different behavioral theories on fishing decisions of various types. Excerpts from ethnographic interviews of groundfish fishermen in New England are used to illustrate how these fishermen deal with uncertainty in decisions they make about when, where, how, and how long to fish. The interviews provide anecdotal evidence in support of prospect theory and other behavioral hypotheses that appear to contrast with what would be considered rational behavior from a neoclassical economics perspective.Fisheries, risk aversion, prospect theory, uncertainty, heuristics and biases., Community/Rural/Urban Development, Institutional and Behavioral Economics, Research Methods/ Statistical Methods, D01, Q22.,

Stochastic Risk Analysis Of Budgeted Financial Statements

Stochastic modeling of financial statements facilitates risk analysis by explicitly introducing uncertainty for key input variables.  When input variables are modeled as probability distributions, then Monte Carlo simulation can be performed for the budgeted financial statements.  Critical outputs within the financial statements can be displayed with cumulative graphs that show a range of outcomes with its likelihood of occurrence.  Stochastic modeling techniques are superior to scenario analysis in assessing risk and are another innovative use of technology in support of managerial decision-making.  Students for a cost/managerial accounting course reported a better understanding of risk analysis for accounting relationships, and a greater interest in modeling uncertainty in other financial relationships

Chasing Unknown Bandits: Uncertainty Guidance in Learning and Decision Making

In repeated decision problems for which it is possible to learn from experience, people should actively seek out uncertain options, rather than avoid ambiguity or uncertainty, in order to learn and improve future decisions. Research on human behavior in a variety of multiarmed-bandit tasks supports this prediction. Multiarmed-bandit tasks involve repeated decisions between options with initially unknown reward distributions and require a careful balance between learning about relatively unknown options (exploration) and obtaining high immediate rewards (exploitation). Resolving this exploration-exploitation dilemma optimally requires considering not only the estimated value of each option, but also the uncertainty in these estimations. Bayesian learning naturally quantifies uncertainty and hence provides a principled framework to study how humans resolve this dilemma. On the basis of computational modeling and behavioral results in bandit tasks, I argue that human learning, attention, and exploration are guided by uncertainty. These results support Bayesian theories of cognition and underpin the fundamental role of subjective uncertainty in both learning and decision making

Uncertain graph as the model of branching technological process

This paper discusses methods of modeling of branching technological process (BTP) under uncertainty based on uncertain graphs. Simulation of BTP involves the formation of the graph and its modification in case of restructuring of BTP. This formalization takes the form of operations on uncertain graph. To use undefined graph as the model of BTP in decision support system (DSS) the concept of transition uncertainty function between states and transition risk uncertainty functions is introduced. Model of BTP as uncertain graph in combination with certain operations on graphs and operator method of uncertainties conversion is described as convenient basis of creating DSS in management systems of BTP

Surrogate-Assisted Unified Optimization Framework for Investigating Marine Structural Design Under Information Uncertainty.

Structural decisions made in the early stages of marine systems design can have a large impact on future acquisition, maintenance and life-cycle costs. However, owing to the unique nature of early stage marine system design, these critical structure decisions are often made on the basis of incomplete information or knowledge about the design. When coupled with design optimization analysis, the complex, uncertain early stage design environment makes it very difficult to deliver a quantified trade-off analysis for decision making. This work presents a novel decision support method that integrates design optimization, high-fidelity analysis, and modeling of information uncertainty for early stage design and analysis. To support this method this dissertation improves the design optimization methods for marine structures by proposing several novel surrogate modeling techniques and strategies. The proposed work treats the uncertainties that are sourced from limited information in a non-statistical interval uncertainty form. This interval uncertainty is treated as an objective function in an optimization framework in order to explore the impact of information uncertainty on structural design performance. In this examination, the potential structural weight penalty regarding information uncertainty can be quickly identified in early stage, avoiding costly redesign later in the design. This dissertation then continues to explore a balanced computational structure between fidelity and efficiency. A proposed novel variable fidelity approach can be applied to wisely allocate expensive high-fidelity computational simulations. In achieving the proposed capabilities for design optimization, several surrogate modeling methods are developed concerning worst-case estimation, clustered multiple meta-modeling, and mixed variable modeling techniques. These surrogate methods have been demonstrated to significantly improve the efficiency of optimizer in dealing with the challenges of early stage marine structure design.PhDNaval Architecture and Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133365/1/yanliuch_1.pd