Revisiting the Predictability of Performative, Social Events

• URL: http://arxiv.org/abs/2503.11713v1
• Date: Wed, 12 Mar 2025 22:19:33 GMT
• Title: Revisiting the Predictability of Performative, Social Events
• Authors: Juan C. Perdomo,
• Abstract summary: We show that one can always efficiently predict social events accurately, regardless of how predictions influence data.<n>While achievable, we also show that these predictions are often undesirable, highlighting the limitations of previous desiderata.
• Score: 7.170441928038049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social predictions do not passively describe the future; they actively shape it. They inform actions and change individual expectations in ways that influence the likelihood of the predicted outcome. Given these dynamics, to what extent can social events be predicted? This question was discussed throughout the 20th century by authors like Merton, Morgenstern, Simon, and others who considered it a central issue in social science methodology. In this work, we provide a modern answer to this old problem. Using recent ideas from performative prediction and outcome indistinguishability, we establish that one can always efficiently predict social events accurately, regardless of how predictions influence data. While achievable, we also show that these predictions are often undesirable, highlighting the limitations of previous desiderata. We end with a discussion of various avenues forward.

Related papers

• Eliciting Uncertainty in Chain-of-Thought to Mitigate Bias against Forecasting Harmful User Behaviors [29.892041865029803]
  Conversation forecasting tasks a model with predicting the outcome of an unfolding conversation. It can be applied in social media moderation to predict harmful user behaviors before they occur. This paper explores what extent model uncertainty can be used as a tool to mitigate potential biases.
  arXiv  Detail & Related papers  (2024-10-17T15:07:53Z)
• Performative Prediction on Games and Mechanism Design [69.7933059664256]
  We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy.<n>As predictions shape collective outcomes, social welfare arises naturally as a metric of concern.<n>We show how to achieve better trade-offs and use them for mechanism design.
  arXiv  Detail & Related papers  (2024-08-09T16:03:44Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• Performative Prediction: Past and Future [20.177988776870517]
  Self-fulfilling and self-negating predictions are examples of performativity. In machine learning applications, performativity often surfaces as distribution shift. A consequence of performative prediction is a natural equilibrium notion that gives rise to new optimization challenges.
  arXiv  Detail & Related papers  (2023-10-25T13:02:45Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Behavioral Intention Prediction in Driving Scenes: A Survey [70.53285924851767]
  Behavioral Intention Prediction (BIP) simulates a human consideration process and fulfills the early prediction of specific behaviors. This work provides a comprehensive review of BIP from the available datasets, key factors and challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications.
  arXiv  Detail & Related papers  (2022-11-01T11:07:37Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Are socially-aware trajectory prediction models really socially-aware? [75.36961426916639]
  We introduce a socially-attended attack to assess the social understanding of prediction models. An attack is a small yet carefully-crafted perturbations to fail predictors. We show that our attack can be employed to increase the social understanding of state-of-the-art models.
  arXiv  Detail & Related papers  (2021-08-24T17:59:09Z)
• How to "Improve" Prediction Using Behavior Modification [0.0]
  Data science researchers design algorithms, models, and approaches to improve prediction. Predictive accuracy is improved with larger and richer data. platforms can stealthily achieve better prediction accuracy by pushing users' behaviors towards their predicted values. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated.
  arXiv  Detail & Related papers  (2020-08-26T12:39:35Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)
• Measuring Forecasting Skill from Text [15.795144936579627]
  We explore connections between the language people use to describe their predictions and their forecasting skill. We present a number of linguistic metrics which are computed over text associated with people's predictions about the future. We demonstrate that it is possible to accurately predict forecasting skill using a model that is based solely on language.
  arXiv  Detail & Related papers  (2020-06-12T19:04:10Z)
• Performative Prediction [31.876692592395777]
  We develop a framework for performative prediction bringing together concepts from statistics, game theory, and causality. A conceptual novelty is an equilibrium notion we call performative stability. Our main results are necessary and sufficient conditions for the convergence of retraining to a performatively stable point of nearly minimal loss.
  arXiv  Detail & Related papers  (2020-02-16T20:29:42Z)

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.