# Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

arXiv: https://arxiv.org/abs/2205.06483 · HTML: https://mlsanity.com/paper/2205.06483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 141 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020)
- [Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration](https://mlsanity.com/paper/2010.09890/) (2020)
- [The Algorithmic Automation Problem: Prediction, Triage, and Human Effort](https://mlsanity.com/paper/1903.12220/) (2019)
- [Combating Label Noise in Deep Learning Using Abstention](https://mlsanity.com/paper/1905.10964/) (2019)
- [Scalable Fair Clustering](https://mlsanity.com/paper/1902.03519/) (2019)
- [Thinking Fast and Slow in AI](https://mlsanity.com/paper/2010.06002/) (2020)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- [Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection](https://mlsanity.com/paper/1912.11637/) (2019)
- [AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control](https://mlsanity.com/paper/2010.05772/) (2020)
- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning](https://mlsanity.com/paper/1903.01209/) (2019)
- [Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making](https://mlsanity.com/paper/1806.04959/) (2018)
- [Designing Fair Ranking Schemes](https://mlsanity.com/paper/1712.09752/) (2017)
- [Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction](https://mlsanity.com/paper/1802.09548/) (2018)
- [Downstream Effects of Affirmative Action](https://mlsanity.com/paper/1808.09004/) (2018)
- [Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding](https://mlsanity.com/paper/1809.03702/) (2018)
- [Learning to solve the credit assignment problem](https://mlsanity.com/paper/1906.00889/) (2019)
- [Reinforcement learning with human advice: a survey](https://mlsanity.com/paper/2005.11016/) (2020)
- [How to Support Users in Understanding Intelligent Systems? Structuring the Discussion](https://mlsanity.com/paper/2001.08301/) (2020)
- [Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits](https://mlsanity.com/paper/1307.6134/) (2013)
- [Unpacking Human Teachers' Intentions For Natural Interactive Task Learning](https://mlsanity.com/paper/2102.06755/) (2021)
- [Modeling Human Decision-making: An Overview of the Brussels Quantum Approach](https://mlsanity.com/paper/1807.11036/) (2018)
- [Understanding ACT-R - an Outsider's Perspective](https://mlsanity.com/paper/1306.0125/) (2013)
- [Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks](https://mlsanity.com/paper/1709.02844/) (2017)
- [Equilibrium Computation of Generalized Nash Games: A New Lagrangian-Based Approach](https://mlsanity.com/paper/2106.00109/) (2021)
- [Humans learn too: Better Human-AI Interaction using Optimized Human Inputs](https://mlsanity.com/paper/2009.09266/) (2020)

## Built on by (most cited first, 1 of 1)

- [Explaining Large Language Models Decisions Using Shapley Values](https://mlsanity.com/paper/2404.01332/) (2024)

## Abstract (arXiv)

As we discussed in Part I of this topic, there is a clear desire to model and comprehend human behavior. Given the popular presupposition of human reasoning as the standard for learning and decision-making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. In Part I, we discussed learning methods which generate a model of behavior from exploration of the system and feedback based on the exhibited behavior as well as topics relating to the use of or accounting for beliefs with respect to applicable skills or mental states of others. In this work, we will continue the discussion from the perspective of methods which focus on the assumed cognitive abilities, limitations, and biases demonstrated in human reasoning. We will arrange these topics as follows (i) methods such as cognitive architectures, cognitive heuristics, and related which demonstrate assumptions of limitations on cognitive resources and how that impacts decisions and (ii) methods which generate and utilize representations of bias or uncertainty to model human decision-making or the future outcomes of decisions.