Simulating outcomes of interventions using a multipurpose simulation program based on the Evolutionary Causal Matrices and Markov Chain

TL;DR

This paper presents a Python-based simulation model using Evolutionary Causal Matrices and Markov Chains to predict long-term intervention outcomes from small-scale data, aiding policy decisions.

Contribution

It introduces a novel simulation framework combining these mathematical tools with class-structured modules, demonstrated through pilot implementations.

Findings

Effective long-term outcome predictions from small-scale data

Flexible simulation with real and hypothetical data

Practical tool for researchers and practitioners

Abstract

Predicting long-term outcomes of interventions is necessary for educational and social policy-making processes that might widely influence our society for the long-term. However, performing such predictions based on data from large-scale experiments might be challenging due to the lack of time and resources. In order to address this issue, computer simulations based on Evolutionary Causal Matrices and Markov Chain can be used to predict long-term outcomes with relatively small-scale lab data. In this paper, we introduce Python classes implementing a computer simulation model and presented some pilots implementations demonstrating how the model can be utilized for predicting outcomes of diverse interventions. We also introduce the class-structured simulation module both with real experimental data and with hypothetical data formulated based on social psychological theories. Classes developed and tested in the present study provide researchers and practitioners with a feasible and practical method to simulate intervention outcomes prospectively.