When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning

• URL: http://arxiv.org/abs/2601.04447v1
• Date: Wed, 07 Jan 2026 23:28:29 GMT
• Title: When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning
• Authors: Gal Fybish, Teo Susnjak,
• Abstract summary: This paper provides a comprehensive review of the literature on performative predictions.<n>We provide an overview of the primary mechanisms through which performativity manifests, present a typology of associated risks, and survey the proposed solutions.<n>Our primary contribution is the Performative Strength vs. Impact Matrix" assessment framework.
• Score: 1.3750624267664158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as performative prediction. This dynamic, in which the deployment of the model influences the very outcome it seeks to predict, can lead to unintended consequences, including feedback loops, performance issues, and significant societal risks. While the literature in the field has grown rapidly in recent years, a socio-technical synthesis that systemises the phenomenon concepts and provides practical guidance has been lacking. This Systematisation of Knowledge (SoK) addresses this gap by providing a comprehensive review of the literature on performative predictions. We provide an overview of the primary mechanisms through which performativity manifests, present a typology of associated risks, and survey the proposed solutions offered in the literature. Our primary contribution is the ``Performative Strength vs. Impact Matrix" assessment framework. This practical tool is designed to help practitioners assess the potential influence and severity of performativity on their deployed predictive models and select the appropriate level of algorithmic or human intervention.

Related papers

• Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior [1.223779595809275]
  Antisocial behavior on social media poses growing challenges for platform safety and societal wellbeing.<n>Prior work has primarily focused on detecting harmful content after it appears, but predictive approaches aim to forecast future harmful behaviors.<n>Despite increasing interest, the field remains fragmented, lacking a unified taxonomy or clear synthesis of existing methods.
  arXiv  Detail & Related papers  (2025-07-28T08:27:58Z)
• Locally Explaining Prediction Behavior via Gradual Interventions and Measuring Property Gradients [8.39361023005333]
  Deep learning models achieve high predictive performance but lack intrinsic interpretability.<n>Existing local explainability methods focus on associations, neglecting the causal drivers of model predictions.<n>We introduce a novel framework for local interventional explanations by leveraging recent advances in image-to-image editing models.
  arXiv  Detail & Related papers  (2025-03-07T13:50:37Z)
• 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)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI.<n>It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.<n>We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• Uplift vs. predictive modeling: a theoretical analysis [1.2412255325209152]
  This paper presents a comprehensive treatment of the subject, starting from firm theoretical foundations and highlighting the parameters that influence the performance of the uplift and predictive approaches. The focus of the paper is on a binary outcome case and a binary action, and the paper presents a theoretical analysis of uplift modeling, comparing it with the classical predictive approach.
  arXiv  Detail & Related papers  (2023-09-21T12:59:17Z)
• Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes [72.13373216644021]
  We study the societal impact of machine learning by considering the collection of models that are deployed in a given context. We find deployed machine learning is prone to systemic failure, meaning some users are exclusively misclassified by all models available. These examples demonstrate ecosystem-level analysis has unique strengths for characterizing the societal impact of machine learning.
  arXiv  Detail & Related papers  (2023-07-12T01:11:52Z)
• Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives [3.553493344868414]
  This bulletin is based on the summary of the author's dissertation. Deep learning models have much higher prediction performance than traditional machine learning models. The specific prediction process is still difficult to interpret and/or explain.
  arXiv  Detail & Related papers  (2023-03-24T16:24:08Z)
• 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)
• Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction [59.221668173521884]
  We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
  arXiv  Detail & Related papers  (2022-04-18T09:25:06Z)
• Individual Explanations in Machine Learning Models: A Survey for Practitioners [69.02688684221265]
  The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
  arXiv  Detail & Related papers  (2021-04-09T01:46:34Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough Patents [0.0]
  We introduce a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance. We discuss the potential synergies between the two fields against the backdrop of this, at first glance, target-incompatibility. We are providing a hands-on predictive modeling introduction for a quantitative social science audience while aiming at demystifying computer science jargon.
  arXiv  Detail & Related papers  (2020-03-30T13:06:25Z)

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.