Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps

06/26/2019
by   Osonde Osoba, et al.
0

Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven webs of causality and policy variables. FCMs are fuzzy signed directed graphs that allow degrees of causal influence and event occurrence. Such causal models can simulate a wide range of policy scenarios and decision processes. Their directed loops or cycles directly model causal feedback. Their nonlinear dynamics permit forward-chaining inference from input causes and policy options to output effects. Users can add detailed dynamics and feedback links directly to the causal model or infer them with statistical learning laws. Users can fuse or combine FCMs from multiple experts by weighting and adding the underlying fuzzy edge matrices and do so recursively if needed. The combined FCM tends to better represent domain knowledge as the expert sample size increases if the expert sample approximates a random sample. Many causal models use more restrictive directed acyclic graphs (DAGs) and Bayesian probabilities. DAGs do not model causal feedback because they do not contain closed loops. Combining DAGs also tends to produce cycles and thus tends not to produce a new DAG. Combining DAGs tends to produce a FCM. FCM causal influence is also transitive whereas probabilistic causal influence is not transitive in general. Overall: FCMs trade the numerical precision of probabilistic DAGs for pattern prediction, faster and scalable computation, ease of combination, and richer feedback representation. We show how FCMs can apply to problems of public support for insurgency and terrorism and to US-China conflict relations in Graham Allison's Thucydides-trap framework. The appendix gives the textual justification of the Thucydides-trap FCM. It also extends our earlier theorem [Osoba-Kosko2017] to a more general result that shows the transitive and total causal influence that upstream concept nodes exert on downstream nodes.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/24/2020

Fuzzy Stochastic Timed Petri Nets for Causal properties representation

Imagery is frequently used to model, represent and communicate knowledge...
research
02/13/2013

A Discovery Algorithm for Directed Cyclis Graphs

Directed acyclic graphs have been used fruitfully to represent causal st...
research
03/13/2013

A Probabilistic Network of Predicates

Bayesian networks are directed acyclic graphs representing independence ...
research
02/22/2022

Relational Causal Models with Cycles:Representation and Reasoning

Causal reasoning in relational domains is fundamental to studying real-w...
research
05/01/2020

Constraint-Based Causal Discovery In The Presence Of Cycles

While feedback loops are known to play important roles in many complex s...
research
09/18/2019

Causal Modeling for Fairness in Dynamical Systems

In this work, we present causal directed acyclic graphs (DAGs) as a unif...
research
01/30/2013

Psychological and Normative Theories of Causal Power and the Probabilities of Causes

This paper (1)shows that the best supported current psychological theory...

Please sign up or login with your details

Forgot password? Click here to reset