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Dynamic Decision Making and Race Games  [PDF]
Shipra De,Darryl A. Seale
ISRN Operations Research , 2013, DOI: 10.1155/2013/452162
Abstract: Frequent criticism of dynamic decision making research pertains to the overly complex nature of the decision tasks used in experimentation. To address such concerns, we study dynamic decision making with respect to a simple race game, which has a computable optimal strategy. In this two-player race game, individuals compete to be the first to reach a designated threshold of points. Players alternate rolling a desired quantity of dice. If the number one appears on any of the dice, the player receives no points for his turn; otherwise, the sum of the numbers appearing on the dice is added to the player's score. Results indicate that although players are influenced by the game state when making their decisions, they tend to play too conservatively in comparison to the optimal policy and are influenced by the behavior of their opponents. Improvement in performance was negligible with repeated play. Survey data suggests that this outcome could be due to inadequate time for learning or insufficient player motivation. However, some players approached optimal heuristic strategies, which perform remarkably well. 1. Introduction A great deal of our understanding of judgment and decision making comes from a body of research that examines the dysfunctional consequences and systematic biases of adopting heuristics or “rules of thumb” in decision making. For example, decision makers (DMs) are known for ignoring base rate information, failing to revise opinions, having unwarranted confidence, and harboring hindsight biases, to name a few [1]. This seems to imply that humans are fairly incompetent beings [2]. Yet while this appears to be true in controlled settings, it is not so in real life. Toda points out that “man drives a car, plays complicated games, and organizes society” [1]. So why is there such a disconnect between experimentation and real-world phenomena? While it is clear that people do make mistakes and can, under certain situations, exhibit systematic deviations from rational predictions, this research is criticized for concentrating on discrete incidents often lacking any form of meaningful feedback [1]. Critics contend that judgment is best viewed as a continuous and interactive process that enables DMs to cope with their environment. The claim, made by Jungermann, is that decision makers who appear biased or error-prone in the short run may be quite effective in continuous or natural environments that allow for feedback and periodic adjustment in decision making [3]. Research in dynamic decision making (DDM) is well suited to advance our understanding
The Role of Reward in Dynamic Decision Making  [PDF]
Magda Osman
Frontiers in Neuroscience , 2012, DOI: 10.3389/fnins.2012.00035
Abstract: The present study investigates two aspects of decision making that have yet to be explored within a dynamic environment, (1) comparing the accuracy of cue-outcome knowledge under conditions in which knowledge acquisition is either through Prediction or Choice, and (2) examining the effects of reward on both Prediction and Choice. In the present study participants either learnt about the cue-outcome relations in the environment by choosing cue values in order to maintain an outcome to criterion (Choice-based decision making), or learnt to predict the outcome from seeing changes to the cue values (Prediction-based decision making). During training participants received outcome feedback and one of four types of reward manipulations: Positive Reward, Negative Reward, Both Positive + Negative Reward, No Reward. After training both groups of learners were tested on prediction and choice-based tasks. In the main, the findings revealed that cue-outcome knowledge was more accurate when knowledge acquisition was Choice-based rather than Prediction-based. During learning Negative Reward adversely affected Choice-based decision making while Positive Reward adversely affected predictive-based decision making. During the test phase only performance on tests of choice was adversely affected by having received Positive Reward or Negative Reward during training. This article proposes that the adverse effects of reward may reflect the additional demands placed on processing rewards which compete for cognitive resources required to perform the main goal of the task. This in turn implies that, rather than facilitate decision making, the presentation of rewards can interfere with Choice-based and Prediction-based decisions.
Dynamic scaling regimes of collective decision making  [PDF]
Andreas Gronlund,Petter Holme,Petter Minnhagen
Physics , 2006, DOI: 10.1209/0295-5075/81/28003
Abstract: We investigate a social system of agents faced with a binary choice. We assume there is a correct, or beneficial, outcome of this choice. Furthermore, we assume agents are influenced by others in making their decision, and that the agents can obtain information that may guide them towards making a correct decision. The dynamic model we propose is of nonequilibrium type, converging to a final decision. We run it on random graphs and scale-free networks. On random graphs, we find two distinct regions in terms of the "finalizing time" -- the time until all agents have finalized their decisions. On scale-free networks on the other hand, there does not seem to be any such distinct scaling regions.
PROMETHEE Method and Sensitivity Analysis in the Software Application for the Support of Decision-Making  [cached]
Petr Moldrik,Jiri Gurecky,Leopold Paszek
Advances in Electrical and Electronic Engineering , 2008,
Abstract: PROMETHEE is one of methods, which fall into multi-criteria analysis (MCA). The MCA, as the name itself indicates, deals with the evaluation of particular variants according to several criteria. Developed software application (MCA8) for the support of multi-criteria decision-making was upgraded about PROMETHEE method and a graphic tool, which enables the execution of the sensitivity analysis. This analysis is used to ascertain how a given model output depends upon the input parameters. The MCA8 software application with mentioned graphic upgrade was developed for purposes of solving multi-criteria decision tasks. In the MCA8 is possible to perform sensitivity analysis by a simple form – through column graphs. We can change criteria significances (weights) in these column graphs directly and watch the changes of the order of variants immediately.
Dynamic consistency and decision making under vacuous belief  [PDF]
Phan H. Giang
Computer Science , 2012,
Abstract: The ideas about decision making under ignorance in economics are combined with the ideas about uncertainty representation in computer science. The combination sheds new light on the question of how artificial agents can act in a dynamically consistent manner. The notion of sequential consistency is formalized by adapting the law of iterated expectation for plausibility measures. The necessary and sufficient condition for a certainty equivalence operator for Nehring-Puppe's preference to be sequentially consistent is given. This result sheds light on the models of decision making under uncertainty.
Neural integrators for decision making: A favorable tradeoff between robustness and sensitivity  [PDF]
Nicholas Cain,Andrea K. Barreiro,Michael Shadlen,Eric Shea-Brown
Quantitative Biology , 2011,
Abstract: A key step in many perceptual decision tasks is the integration of sensory inputs over time, but fundamental questions remain about how this is accomplished in neural circuits. One possibility is to balance decay modes of membranes and synapses with recurrent excitation. To allow integration over long timescales, however, this balance must be precise; this is known as the fine tuning problem. The need for fine tuning can be overcome via a ratchet-like mechanism, in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this ratcheting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. The goal of our study is to analyze the consequences of this tradeoff for decision making performance. For concreteness, we focus on the well-studied random dot motion discrimination task. For stimulus parameters constrained by experimental data, we find that loss of sensitivity to inputs has surprisingly little cost for decision performance. This leads robust integrators to performance gains when feedback becomes mistuned. Moreover, we find that substantially robust and mistuned integrator models remain consistent with chronometric and accuracy functions found in experiments. We explain our findings via sequential analysis of the momentary and integrated signals, and discuss their implication: robust integrators may be surprisingly well-suited to subserve the basic function of evidence integration in many cognitive tasks.
Dynamic Teaching in Sequential Decision Making Environments  [PDF]
Thomas J. Walsh,Sergiu Goschin
Computer Science , 2012,
Abstract: We describe theoretical bounds and a practical algorithm for teaching a model by demonstration in a sequential decision making environment. Unlike previous efforts that have optimized learners that watch a teacher demonstrate a static policy, we focus on the teacher as a decision maker who can dynamically choose different policies to teach different parts of the environment. We develop several teaching frameworks based on previously defined supervised protocols, such as Teaching Dimension, extending them to handle noise and sequences of inputs encountered in an MDP.We provide theoretical bounds on the learnability of several important model classes in this setting and suggest a practical algorithm for dynamic teaching.
Time-Critical Dynamic Decision Making  [PDF]
Yanping Xiang,Kim-Leng Poh
Computer Science , 2013,
Abstract: Recent interests in dynamic decision modeling have led to the development of several representation and inference methods. These methods however, have limited application under time critical conditions where a trade-off between model quality and computational tractability is essential. This paper presents an approach to time-critical dynamic decision modeling. A knowledge representation and modeling method called the time-critical dynamic influence diagram is proposed. The formalism has two forms. The condensed form is used for modeling and model abstraction, while the deployed form which can be converted from the condensed form is used for inference purposes. The proposed approach has the ability to represent space-temporal abstraction within the model. A knowledge-based meta-reasoning approach is proposed for the purpose of selecting the best abstracted model that provide the optimal trade-off between model quality and model tractability. An outline of the knowledge-based model construction algorithm is also provided.
Sensitivity and Bias in Decision-Making under Risk: Evaluating the Perception of Reward, Its Probability and Value  [PDF]
Madeleine E. Sharp, Jayalakshmi Viswanathan, Linda J. Lanyon, Jason J. S. Barton
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0033460
Abstract: Background There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. Objective We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. Design/Methods Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. Results Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a ‘risk premium’ of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. Conclusions This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia.
Dynamic Integration of Reward and Stimulus Information in Perceptual Decision-Making  [PDF]
Juan Gao,Rebecca Tortell,James L. McClelland
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0016749
Abstract: In perceptual decision-making, ideal decision-makers should bias their choices toward alternatives associated with larger rewards, and the extent of the bias should decrease as stimulus sensitivity increases. When responses must be made at different times after stimulus onset, stimulus sensitivity grows with time from zero to a final asymptotic level. Are decision makers able to produce responses that are more biased if they are made soon after stimulus onset, but less biased if they are made after more evidence has been accumulated? If so, how close to optimal can they come in doing this, and how might their performance be achieved mechanistically? We report an experiment in which the payoff for each alternative is indicated before stimulus onset. Processing time is controlled by a “go” cue occurring at different times post stimulus onset, requiring a response within msec. Reward bias does start high when processing time is short and decreases as sensitivity increases, leveling off at a non-zero value. However, the degree of bias is sub-optimal for shorter processing times. We present a mechanistic account of participants' performance within the framework of the leaky competing accumulator model [1], in which accumulators for each alternative accumulate noisy information subject to leakage and mutual inhibition. The leveling off of accuracy is attributed to mutual inhibition between the accumulators, allowing the accumulator that gathers the most evidence early in a trial to suppress the alternative. Three ways reward might affect decision making in this framework are considered. One of the three, in which reward affects the starting point of the evidence accumulation process, is consistent with the qualitative pattern of the observed reward bias effect, while the other two are not. Incorporating this assumption into the leaky competing accumulator model, we are able to provide close quantitative fits to individual participant data.
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