252,728 research outputs found
Planning with Multiple Biases
Recent work has considered theoretical models for the behavior of agents with
specific behavioral biases: rather than making decisions that optimize a given
payoff function, the agent behaves inefficiently because its decisions suffer
from an underlying bias. These approaches have generally considered an agent
who experiences a single behavioral bias, studying the effect of this bias on
the outcome.
In general, however, decision-making can and will be affected by multiple
biases operating at the same time. How do multiple biases interact to produce
the overall outcome? Here we consider decisions in the presence of a pair of
biases exhibiting an intuitively natural interaction: present bias -- the
tendency to value costs incurred in the present too highly -- and sunk-cost
bias -- the tendency to incorporate costs experienced in the past into one's
plans for the future.
We propose a theoretical model for planning with this pair of biases, and we
show how certain natural behavioral phenomena can arise in our model only when
agents exhibit both biases. As part of our model we differentiate between
agents that are aware of their biases (sophisticated) and agents that are
unaware of them (naive). Interestingly, we show that the interaction between
the two biases is quite complex: in some cases, they mitigate each other's
effects while in other cases they might amplify each other. We obtain a number
of further results as well, including the fact that the planning problem in our
model for an agent experiencing and aware of both biases is computationally
hard in general, though tractable under more relaxed assumptions
Integrating multicriteria decision analysis and scenario planning : review and extension
Scenario planning and multiple criteria decision analysis (MCDA) are two key management science tools used in strategic planning. In this paper, we explore the integration of these two approaches in a coherent manner, recognizing that each adds value to the implementation of the other. Various approaches that have been adopted for such integration are reviewed, with a primary focus on the process of constructing preferences both within and between scenarios. Biases that may be introduced by inappropriate assumptions during such processes are identified, and used to motivate a framework for integrating MCDA and scenario thinking, based on applying MCDA concepts across a range of "metacriteria" (combinations of scenarios and primary criteria). Within this framework, preferences according to each primary criterion can be expressed in the context of different scenarios. The paper concludes with a hypothetical but non-trivial example of agricultural policy planning in a developing country
Cross-screening in observational studies that test many hypotheses
We discuss observational studies that test many causal hypotheses, either
hypotheses about many outcomes or many treatments. To be credible an
observational study that tests many causal hypotheses must demonstrate that its
conclusions are neither artifacts of multiple testing nor of small biases from
nonrandom treatment assignment. In a sense that needs to be defined carefully,
hidden within a sensitivity analysis for nonrandom assignment is an enormous
correction for multiple testing: in the absence of bias, it is extremely
improbable that multiple testing alone would create an association insensitive
to moderate biases. We propose a new strategy called "cross-screening",
different from but motivated by recent work of Bogomolov and Heller on
replicability. Cross-screening splits the data in half at random, uses the
first half to plan a study carried out on the second half, then uses the second
half to plan a study carried out on the first half, and reports the more
favorable conclusions of the two studies correcting using the Bonferroni
inequality for having done two studies. If the two studies happen to concur,
then they achieve Bogomolov-Heller replicability; however, importantly,
replicability is not required for strong control of the family-wise error rate,
and either study alone suffices for firm conclusions. In randomized studies
with a few hypotheses, cross-split screening is not an attractive method when
compared with conventional methods of multiplicity control, but it can become
attractive when hundreds or thousands of hypotheses are subjected to
sensitivity analyses in an observational study. We illustrate the technique by
comparing 46 biomarkers in individuals who consume large quantities of fish
versus little or no fish.Comment: 33 pages, 2 figures, 5 table
Reducing bias in auditory duration reproduction by integrating the reproduced signal
Duration estimation is known to be far from veridical and to differ for sensory estimates and motor reproduction. To investigate how these differential estimates are integrated for estimating or reproducing a duration and to examine sensorimotor biases in duration comparison and reproduction tasks, we compared estimation biases and variances among three different duration estimation tasks: perceptual comparison, motor reproduction, and auditory reproduction (i.e. a combined perceptual-motor task). We found consistent overestimation in both motor and perceptual-motor auditory reproduction tasks, and the least overestimation in the comparison task. More interestingly, compared to pure motor reproduction, the overestimation bias was reduced in the auditory reproduction task, due to the additional reproduced auditory signal. We further manipulated the signal-to-noise ratio (SNR) in the feedback/comparison tones to examine the changes in estimation biases and variances. Considering perceptual and motor biases as two independent components, we applied the reliability-based model, which successfully predicted the biases in auditory reproduction. Our findings thus provide behavioral evidence of how the brain combines motor and perceptual information together to reduce duration estimation biases and improve estimation reliability
foresight for crisis prevention
As part of their efforts to professionalize crisis and conflict prevention, foreign policy-makers are investing more in foresight, early warning or prediction. Different approaches and their products are suited for different purposes, based on distinct strengths and weaknesses. This policy paper provides an overview of the most common methods used in the context of preventing violent conflict and governance breakdown, and offers guidance on what to look out for when thinking about and planning for the future of crisis prevention
Problem formulation and organizational decision-making : biases and assumptions underlying alternative models of strategic problem formulation
Bibliography: p. 20-25
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Are Women In Lomé Getting Their Desired Methods Of Contraception? Understanding Provider Bias From Restrictions To Choice.
Background: Despite improvements in contraception availability, women face persistent barriers that compromise reproductive autonomy and informed choice. Provider bias is one way in which access to contraception can be restricted within clinical encounters and has been established as common in sub-Saharan Africa. This analysis assessed the prevalence of provider restrictions and the potential impact on womens method uptake in Lomé, Togo. Methods: This sub-analysis used survey data from provider and client interviews collected to assess the impacts of the Agir pour la Planification Familiale (AgirPF) program in Togo. The relationships between provider restrictiveness and womens receipt of their desired method of contraception were modelled using mixed effects logistic regressions looking at all women and among subgroups hypothesized to be at potentially higher risk of bias. Results: Around 84% of providers reported a restriction in contraceptive provision for the five contraceptive methods explored (pill, male condom, injectable, IUD, and implant). Around 53% of providers reported restricting at least four of the five methods based on age, parity, partner consent, or marital status. Among all women, there were no significant associations between provider restrictiveness and womens receipt of desired method, including among those who desired long-acting methods. In adjusted modeling, marital status was a covariate significantly associated with desired method, with married women more likely to receive their desired method than unmarried women (aOR 2.73, 95% CI 1.45-5.13). Conclusion: Provider reports of high levels of restrictions in this population are concerning and should be further explored, especially its effects on unmarried women. However, restrictions reported by providers in this study did not appear to statistically significantly influence contraceptive method received
Learning the Preferences of Ignorant, Inconsistent Agents
An important use of machine learning is to learn what people value. What
posts or photos should a user be shown? Which jobs or activities would a person
find rewarding? In each case, observations of people's past choices can inform
our inferences about their likes and preferences. If we assume that choices are
approximately optimal according to some utility function, we can treat
preference inference as Bayesian inverse planning. That is, given a prior on
utility functions and some observed choices, we invert an optimal
decision-making process to infer a posterior distribution on utility functions.
However, people often deviate from approximate optimality. They have false
beliefs, their planning is sub-optimal, and their choices may be temporally
inconsistent due to hyperbolic discounting and other biases. We demonstrate how
to incorporate these deviations into algorithms for preference inference by
constructing generative models of planning for agents who are subject to false
beliefs and time inconsistency. We explore the inferences these models make
about preferences, beliefs, and biases. We present a behavioral experiment in
which human subjects perform preference inference given the same observations
of choices as our model. Results show that human subjects (like our model)
explain choices in terms of systematic deviations from optimal behavior and
suggest that they take such deviations into account when inferring preferences.Comment: AAAI 201
Options for basing Dietary Reference Intakes (DRIs) on chronic disease endpoints: report from a joint US-/Canadian-sponsored working group.
Dietary Reference Intakes (DRIs) are used in Canada and the United States in planning and assessing diets of apparently healthy individuals and population groups. The approaches used to establish DRIs on the basis of classical nutrient deficiencies and/or toxicities have worked well. However, it has proved to be more challenging to base DRI values on chronic disease endpoints; deviations from the traditional framework were often required, and in some cases, DRI values were not established for intakes that affected chronic disease outcomes despite evidence that supported a relation. The increasing proportions of elderly citizens, the growing prevalence of chronic diseases, and the persistently high prevalence of overweight and obesity, which predispose to chronic disease, highlight the importance of understanding the impact of nutrition on chronic disease prevention and control. A multidisciplinary working group sponsored by the Canadian and US government DRI steering committees met from November 2014 to April 2016 to identify options for addressing key scientific challenges encountered in the use of chronic disease endpoints to establish reference values. The working group focused on 3 key questions: 1) What are the important evidentiary challenges for selecting and using chronic disease endpoints in future DRI reviews, 2) what intake-response models can future DRI committees consider when using chronic disease endpoints, and 3) what are the arguments for and against continuing to include chronic disease endpoints in future DRI reviews? This report outlines the range of options identified by the working group for answering these key questions, as well as the strengths and weaknesses of each option
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