Precarity: Modeling the Long Term Effects of Compounded Decisions on
Individual Instability
- URL: http://arxiv.org/abs/2104.12037v1
- Date: Sat, 24 Apr 2021 23:38:07 GMT
- Title: Precarity: Modeling the Long Term Effects of Compounded Decisions on
Individual Instability
- Authors: Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Neal Patwari, Suresh
Venkatasubramanian
- Abstract summary: There has hardly been any focus on precarity which is the term that encapsulates the instability in people's lives.
A negative outcome can overspread to other decisions and measures of well-being.
We propose a modeling framework that simulates the effects of compounded decision-making on precarity over time.
- Score: 7.993424873879106
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: When it comes to studying the impacts of decision making, the research has
been largely focused on examining the fairness of the decisions, the long-term
effects of the decision pipelines, and utility-based perspectives considering
both the decision-maker and the individuals. However, there has hardly been any
focus on precarity which is the term that encapsulates the instability in
people's lives. That is, a negative outcome can overspread to other decisions
and measures of well-being. Studying precarity necessitates a shift in focus -
from the point of view of the decision-maker to the perspective of the decision
subject. This centering of the subject is an important direction that unlocks
the importance of parting with aggregate measures to examine the long-term
effects of decision making. To address this issue, in this paper, we propose a
modeling framework that simulates the effects of compounded decision-making on
precarity over time. Through our simulations, we are able to show the
heterogeneity of precarity by the non-uniform ruinous aftereffects of negative
decisions on different income classes of the underlying population and how
policy interventions can help mitigate such effects.
Related papers
- Decision Theoretic Foundations for Experiments Evaluating Human
Decisions [20.5402873175161]
We present a widely applicable definition of a decision problem synthesized from statistical decision theory and information economics.
We argue that to attribute loss in human performance to forms of bias, an experiment must provide participants with the information that a rational agent would need to identify the normative decision.
arXiv Detail & Related papers (2024-01-25T16:21:37Z) - Navigating Decision Landscapes: The Impact of Principals on
Decision-Making Dynamics [6.780877976424507]
Our study introduced principals or external guides, adding to the decision-making process.
The reliability of these principals significantly influenced decisions.
Our findings emphasize the need for caution when placing trust in decision-making contexts.
arXiv Detail & Related papers (2023-12-25T00:24:29Z) - RISE: Robust Individualized Decision Learning with Sensitive Variables [1.5293427903448025]
A naive baseline is to ignore sensitive variables in learning decision rules, leading to significant uncertainty and bias.
We propose a decision learning framework to incorporate sensitive variables during offline training but not include them in the input of the learned decision rule during model deployment.
arXiv Detail & Related papers (2022-11-12T04:31:38Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Inverse Online Learning: Understanding Non-Stationary and Reactionary
Policies [79.60322329952453]
We show how to develop interpretable representations of how agents make decisions.
By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem.
We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them.
Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
arXiv Detail & Related papers (2022-03-14T17:40:42Z) - Differential Privacy and Fairness in Decisions and Learning Tasks: A
Survey [50.90773979394264]
It reviews the conditions under which privacy and fairness may have aligned or contrasting goals.
It analyzes how and why DP may exacerbate bias and unfairness in decision problems and learning tasks.
arXiv Detail & Related papers (2022-02-16T16:50:23Z) - The Impact of Algorithmic Risk Assessments on Human Predictions and its
Analysis via Crowdsourcing Studies [79.66833203975729]
We conduct a vignette study in which laypersons are tasked with predicting future re-arrests.
Our key findings are as follows: Participants often predict that an offender will be rearrested even when they deem the likelihood of re-arrest to be well below 50%.
Judicial decisions, unlike participants' predictions, depend in part on factors that are to the likelihood of re-arrest.
arXiv Detail & Related papers (2021-09-03T11:09:10Z) - Addressing the Long-term Impact of ML Decisions via Policy Regret [49.92903850297013]
We study a setting in which the reward from each arm evolves every time the decision-maker pulls that arm.
We argue that an acceptable sequential allocation of opportunities must take an arm's potential for growth into account.
We present an algorithm with provably sub-linear policy regret for sufficiently long time horizons.
arXiv Detail & Related papers (2021-06-02T17:38:10Z) - Inverse Active Sensing: Modeling and Understanding Timely
Decision-Making [111.07204912245841]
We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure.
We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
arXiv Detail & Related papers (2020-06-25T02:30:45Z) - Fairness in Learning-Based Sequential Decision Algorithms: A Survey [22.252241233231263]
We will focus on two types of sequential decisions: past decisions have no impact on the underlying user population and thus no impact on future data.
In each case the impact of various fairness interventions on the underlying population is examined.
arXiv Detail & Related papers (2020-01-14T15:49:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.