arXiv:2605.23978v1 Announce Type: new Abstract: In algorithmic markets, predictive models become part of the data-generating process they aim to forecast. Once their outputs are converted into trades, allocations, execution schedules, or risk controls, they change the future data on which they are evaluated. I introduce algometrics, a framework for time series whose evolution depends on the predictive algorithms forecasting them. The framework distinguishes historical risk, measured under passive forecasting, from deployment risk, measured when forecasts drive actions. I prove three results. F
The proliferation of AI in decision-making systems across various sectors necessitates a deeper understanding of its feedback loops and their impact on data generation.
This research provides a framework to differentiate between historical and deployment risks, crucial for designing robust, ethical, and effective algorithmic systems that minimize unintended consequences.
The explicit recognition and framework for 'algometrics' shifts the paradigm from passive forecasting to active, feedback-driven forecasting, altering how models are evaluated and deployed.
- · AI ethicists
- · Quantitative analysts
- · Regulators
- · Financial institutions implementing AI
- · Organizations using unmonitored AI
- · Legacy forecasting models
- · Naive AI deployers
More sophisticated and self-aware AI systems will emerge, designed to account for their impact on the environment they operate within.
New regulatory frameworks and compliance standards will develop to mandate algometric analysis for critical AI deployments.
The concept of 'objective' data will be increasingly challenged as the influence of predictive models on data generation becomes widely understood, leading to more dynamic and adaptive data governance.
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