---
title: "Higher-Order Potentialities and their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods"
authors:
  - "Tibor Bosse"
  - "Jan Treur"
doi: ""
url: "https://ijcai.org/proceedings/2007/40"
pdf: "https://ijcai.org/proceedings/2007/0040.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "262-266"
track: ""
keywords:
---

# Higher-Order Potentialities and their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods

Tibor Bosse, Jan Treur

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 262-266.

## Abstract

In the development of disciplines addressing dynamics, a major role was played by the assumption that processes can be modelled by introducing state properties, called potentialities, anticipating in which respect a next state will be different. A second assumption often made is that these state properties can be related to other state properties, called reducers. The current paper proposes a philosophical framework in terms of potentialities and their reducers to obtain a common philosophical foundation for methods in AI and Cognitive Science to model dynamics. This framework provides a unified philosophical foundation for numerical, symbolic, and hybrid approaches.