Neural networks and agent-based diffusion models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper introduces a new consumer decision-making model where each agent uses a neural network to evaluate word-of-mouth and predict her utility prior to adoption a new product based on her experiences in the past. The model considers the fact that consumers may not know their true preferences before experiencing the product. By using a neural network, an agent can: (1) interpret the feedback from a neighbor who has conflicting preferences with her; (2) interpret partially positive and/or negative feedback; and, (3) assign different weights to the feedback received from different neighbors. The model is implemented in an agent-based simulation model to verify that the resulting diffusion dynamics follow a typical diffusion curve. Preliminary experiments with the model also provide interesting results about the effect of the number of product attributes on the quality of an individual's utility prediction as well as proportion of satisfied adopters.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1407-1418
Number of pages12
ISBN (Electronic)9781538634288
DOIs
StatePublished - Jan 4 2018
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2017 Winter Simulation Conference, WSC 2017
CountryUnited States
CityLas Vegas
Period12/3/1712/6/17

Fingerprint

Agent-based Model
Diffusion Model
Neural Networks
Neural networks
Feedback
Negative Feedback
Agent-based Simulation
Model
Assign
Simulation Model
Proportion
Decision Making
Attribute
Verify
Predict
Curve
Decision making
Evaluate
Prediction
Experiment

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Cite this

Negahban, A. (2018). Neural networks and agent-based diffusion models. In V. Chan (Ed.), 2017 Winter Simulation Conference, WSC 2017 (pp. 1407-1418). (Proceedings - Winter Simulation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2017.8247884
Negahban, Ashkan. / Neural networks and agent-based diffusion models. 2017 Winter Simulation Conference, WSC 2017. editor / Victor Chan. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1407-1418 (Proceedings - Winter Simulation Conference).
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Negahban, A 2018, Neural networks and agent-based diffusion models. in V Chan (ed.), 2017 Winter Simulation Conference, WSC 2017. Proceedings - Winter Simulation Conference, Institute of Electrical and Electronics Engineers Inc., pp. 1407-1418, 2017 Winter Simulation Conference, WSC 2017, Las Vegas, United States, 12/3/17. https://doi.org/10.1109/WSC.2017.8247884

Neural networks and agent-based diffusion models. / Negahban, Ashkan.

2017 Winter Simulation Conference, WSC 2017. ed. / Victor Chan. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1407-1418 (Proceedings - Winter Simulation Conference).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Negahban A. Neural networks and agent-based diffusion models. In Chan V, editor, 2017 Winter Simulation Conference, WSC 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1407-1418. (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2017.8247884