Kairosis: A method for dynamical probability forecast aggregation informed by Bayesian change point detection

Zane Hassoun; Niall MacKay; Ben Powell

Kairosis: A method for dynamical probability forecast aggregation informed by Bayesian change point detection

Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

We present a new method, "kairosis", for aggregating probability forecasts made over a time period of a single outcome determined at the end of that period. Informed by work on Bayesian change-point detection, we begin by constructing for each time during the period a posterior probability that the forecasts before and after this time are distributed differently. The resulting posterior probability mass function is integrated to give a cumulative mass function, which is used to create a weighted median forecast. The effect is to construct an aggregate in which the most heavily weighted forecasts are those which have been made since the probable most recent change in the forecasts' distribution. Kairosis outperforms standard methods, and is especially suitable for geopolitical forecasting tournaments because it is observed to be robust across disparate questions and forecaster distributions.

Original language	English
Journal	International journal of forecasting
Publication status	Accepted/In press - 8 Mar 2025

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

Embargoed Document

Kairosis_AAMS_With_Authors
Accepted author manuscript, 1.21 MB
Licence: CC BY
Embargo ends: 31/12/99

Cite this

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title = "Kairosis: A method for dynamical probability forecast aggregation informed by Bayesian change point detection",

abstract = "We present a new method, {"}kairosis{"}, for aggregating probability forecasts made over a time period of a single outcome determined at the end of that period. Informed by work on Bayesian change-point detection, we begin by constructing for each time during the period a posterior probability that the forecasts before and after this time are distributed differently. The resulting posterior probability mass function is integrated to give a cumulative mass function, which is used to create a weighted median forecast. The effect is to construct an aggregate in which the most heavily weighted forecasts are those which have been made since the probable most recent change in the forecasts' distribution. Kairosis outperforms standard methods, and is especially suitable for geopolitical forecasting tournaments because it is observed to be robust across disparate questions and forecaster distributions.",

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T1 - Kairosis: A method for dynamical probability forecast aggregation informed by Bayesian change point detection

AU - Hassoun, Zane

AU - MacKay, Niall

AU - Powell, Ben

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

PY - 2025/3/8

Y1 - 2025/3/8

N2 - We present a new method, "kairosis", for aggregating probability forecasts made over a time period of a single outcome determined at the end of that period. Informed by work on Bayesian change-point detection, we begin by constructing for each time during the period a posterior probability that the forecasts before and after this time are distributed differently. The resulting posterior probability mass function is integrated to give a cumulative mass function, which is used to create a weighted median forecast. The effect is to construct an aggregate in which the most heavily weighted forecasts are those which have been made since the probable most recent change in the forecasts' distribution. Kairosis outperforms standard methods, and is especially suitable for geopolitical forecasting tournaments because it is observed to be robust across disparate questions and forecaster distributions.

AB - We present a new method, "kairosis", for aggregating probability forecasts made over a time period of a single outcome determined at the end of that period. Informed by work on Bayesian change-point detection, we begin by constructing for each time during the period a posterior probability that the forecasts before and after this time are distributed differently. The resulting posterior probability mass function is integrated to give a cumulative mass function, which is used to create a weighted median forecast. The effect is to construct an aggregate in which the most heavily weighted forecasts are those which have been made since the probable most recent change in the forecasts' distribution. Kairosis outperforms standard methods, and is especially suitable for geopolitical forecasting tournaments because it is observed to be robust across disparate questions and forecaster distributions.

M3 - Article

SN - 0169-2070

JO - International journal of forecasting

JF - International journal of forecasting

ER -