Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach

• URL: http://arxiv.org/abs/2005.12638v2
• Date: Wed, 2 Dec 2020 15:50:58 GMT
• Title: Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach
• Authors: Dainis Zegners, Uwe Sunde, Anthony Strittmatter
• Abstract summary: This paper presents a novel approach to analyze human decision-making. It involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality. This benchmark is constructed using algorithms of modern chess engines and allows investigating behavior at the level of individual move-by-move observations, thus representing a natural benchmark for computationally bounded optimization. The analysis delivers novel insights by isolating deviations from this benchmark of bounded rationality as well as their causes and consequences for performance. The findings document the existence of several distinct dimensions of behavioral deviations, which are related to asymmetric positional evaluation in terms of losses and gains, time pressure, fatigue, and complexity. The results also document that deviations from the benchmark do not necessarily entail worse performance. Faster decisions are associated with more frequent deviations from the benchmark, yet they are also associated with better performance. The findings are consistent with an important influence of intuition and experience, thereby shedding new light on the recent debate about computational rationality in cognitive processes.

Related papers

• Switchable Decision: Dynamic Neural Generation Networks [98.61113699324429]
  We propose a switchable decision to accelerate inference by dynamically assigning resources for each data instance. Our method benefits from less cost during inference while keeping the same accuracy.
  arXiv  Detail & Related papers  (2024-05-07T17:44:54Z)
• Efficient Benchmarking of Language Models [22.696230279151166]
  We present the problem of Efficient Benchmarking, namely, intelligently reducing the costs of LM evaluation without compromising reliability. Using the HELM benchmark as a test case, we investigate how different benchmark design choices affect the computation-reliability trade-off. We propose an evaluation algorithm, that, when applied to the HELM benchmark, leads to dramatic cost savings with minimal loss of benchmark reliability.
  arXiv  Detail & Related papers  (2023-08-22T17:59:30Z)
• Provably Efficient Learning in Partially Observable Contextual Bandit [4.910658441596583]
  We show how causal bounds can be applied to improving classical bandit algorithms. This research has the potential to enhance the performance of contextual bandit agents in real-world applications.
  arXiv  Detail & Related papers  (2023-08-07T13:24:50Z)
• Are metaheuristics worth it? A computational comparison between nature-inspired and deterministic techniques on black-box optimization problems [0.0]
  In this paper, we provide an extensive computational comparison of selected methods from each of these branches. The results showed that, when dealing with situations where the objective function evaluations are relatively cheap, the nature-inspired methods have a significantly better performance than their deterministic counterparts.
  arXiv  Detail & Related papers  (2022-12-13T19:44:24Z)
• Multi-Target Decision Making under Conditions of Severe Uncertainty [0.0]
  We show how incomplete preferential and probabilistic information can be exploited to compare decisions among different targets. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in the context of comparing algorithms under different performance measures.
  arXiv  Detail & Related papers  (2022-12-13T11:47:02Z)
• Relational Surrogate Loss Learning [41.61184221367546]
  This paper revisits the surrogate loss learning, where a deep neural network is employed to approximate the evaluation metrics. In this paper, we show that directly maintaining the relation of models between surrogate losses and metrics suffices. Our method is much easier to optimize and enjoys significant efficiency and performance gains.
  arXiv  Detail & Related papers  (2022-02-26T17:32:57Z)
• Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators [91.3755431537592]
  We show that even in arguably the simplest setting, estimation under ignorability assumptions can be misleading. We consider two popular machine learning benchmark datasets for evaluation of heterogeneous treatment effect estimators. We highlight that the inherent characteristics of the benchmark datasets favor some algorithms over others.
  arXiv  Detail & Related papers  (2021-07-28T13:21:27Z)
• Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions [51.8695223602729]
  adversarial attack methods have been developed to challenge the robustness of machine learning models. We propose a Piece-wise Sampling Curving (PSC) toolkit to effectively address the discrepancy. PSC toolkit offers options for balancing the computational cost and evaluation effectiveness.
  arXiv  Detail & Related papers  (2021-04-22T14:36:51Z)
• Loss Bounds for Approximate Influence-Based Abstraction [81.13024471616417]
  Influence-based abstraction aims to gain leverage by modeling local subproblems together with the 'influence' that the rest of the system exerts on them. This paper investigates the performance of such approaches from a theoretical perspective. We show that neural networks trained with cross entropy are well suited to learn approximate influence representations.
  arXiv  Detail & Related papers  (2020-11-03T15:33:10Z)
• Invariant Rationalization [84.1861516092232]
  A typical rationalization criterion, i.e. maximum mutual information (MMI), finds the rationale that maximizes the prediction performance based only on the rationale. We introduce a game-theoretic invariant rationalization criterion where the rationales are constrained to enable the same predictor to be optimal across different environments. We show both theoretically and empirically that the proposed rationales can rule out spurious correlations, generalize better to different test scenarios, and align better with human judgments.
  arXiv  Detail & Related papers  (2020-03-22T00:50:27Z)
• Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading [96.48553941812366]
  Lip-reading aims to infer the speech content from the lip movement sequence. Traditional learning process of seq2seq models suffers from two problems. We propose a novel pseudo-convolutional policy gradient (PCPG) based method to address these two problems.
  arXiv  Detail & Related papers  (2020-03-09T09:12:26Z)

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.