Catastrophe, Compounding & Consistency in Choice
- URL: http://arxiv.org/abs/2111.06804v1
- Date: Fri, 12 Nov 2021 16:33:06 GMT
- Title: Catastrophe, Compounding & Consistency in Choice
- Authors: Chris Gagne and Peter Dayan
- Abstract summary: Conditional value-at-risk (CVaR) precisely characterizes the influence that rare, catastrophic events can exert over decisions.
These examples can ground future experiments with the broader aim of characterizing risk attitudes.
- Score: 4.974890682815778
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conditional value-at-risk (CVaR) precisely characterizes the influence that
rare, catastrophic events can exert over decisions. Such characterizations are
important for both normal decision-making and for psychiatric conditions such
as anxiety disorders -- especially for sequences of decisions that might
ultimately lead to disaster. CVaR, like other well-founded risk measures,
compounds in complex ways over such sequences -- and we recently formalized
three structurally different forms in which risk either averages out or
multiplies. Unfortunately, existing cognitive tasks fail to discriminate these
approaches well; here, we provide examples that highlight their unique
characteristics, and make formal links to temporal discounting for the two of
the approaches that are time consistent. These examples can ground future
experiments with the broader aim of characterizing risk attitudes, especially
for longer horizon problems and in psychopathological populations.
Related papers
- HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks [51.95824566163554]
HACSurv is a survival analysis method that learns structures and cause-specific survival functions from data with competing risks.
By capturing the dependencies between risks and censoring, HACSurv achieves better survival predictions.
arXiv Detail & Related papers (2024-10-19T18:52:18Z) - On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
Temporally causal representation learning aims to identify the latent causal process from time series observations.
Most methods require the assumption that the latent causal processes do not have instantaneous relations.
We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
arXiv Detail & Related papers (2024-05-24T08:08:05Z) - Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction [55.77015419028725]
We develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively.
Our methodology supports monotone and nearly-monotone risks, but otherwise makes no distributional assumptions.
arXiv Detail & Related papers (2024-03-28T17:28:06Z) - Two Types of AI Existential Risk: Decisive and Accumulative [3.5051464966389116]
This paper contrasts the conventional "decisive AI x-risk hypothesis" with an "accumulative AI x-risk hypothesis"
The accumulative hypothesis suggests a boiling frog scenario where incremental AI risks slowly converge, undermining resilience until a triggering event results in irreversible collapse.
arXiv Detail & Related papers (2024-01-15T17:06:02Z) - Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks [142.67349734180445]
Existing algorithms that provide risk-awareness to deep neural networks are complex and ad-hoc.
Here we present capsa, a framework for extending models with risk-awareness.
arXiv Detail & Related papers (2023-08-01T02:07:47Z) - Continuous Risk Measures for Driving Support [0.0]
We compare three model-based risk measures by evaluating their stengths and qualitatively testing them quantitatively.
We derive a novel risk measure based on the statistics of sparse critical events and so-called survival conditions.
The resulting survival analysis shows to have an earlier detection time crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding.
arXiv Detail & Related papers (2023-03-14T15:54:37Z) - Two steps to risk sensitivity [4.974890682815778]
conditional value-at-risk (CVaR) is a risk measure for modeling human and animal planning.
We adopt a conventional distributional approach to CVaR in a sequential setting and reanalyze the choices of human decision-makers.
We then consider a further critical property of risk sensitivity, namely time consistency, showing alternatives to this form of CVaR.
arXiv Detail & Related papers (2021-11-12T16:27:47Z) - SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event
Data [83.50281440043241]
We study the problem of inferring heterogeneous treatment effects from time-to-event data.
We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
arXiv Detail & Related papers (2021-10-26T20:13:17Z) - Enabling risk-aware Reinforcement Learning for medical interventions
through uncertainty decomposition [9.208828373290487]
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems.
It is often challenging to bridge the gap between an apparently optimal policy learnt by an agent and its real-world deployment.
Here we propose how a distributional approach (UA-DQN) can be recast to render uncertainties by decomposing the net effects of each uncertainty.
arXiv Detail & Related papers (2021-09-16T09:36:53Z) - A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models.
We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
arXiv Detail & Related papers (2020-06-08T19:24:54Z)
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.