Simulation of Trust in Client: Wealth Management Advisor Relationships

Terence Bossomaier; Russell Standish

Simulation of Trust in Client: Wealth Management Advisor Relationships

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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Abstract

This paper describes a two phase model for simulating trust amongst clients and their wealth management advisors. In phase one an artifcial life model was used to assess the dynamics of trust. In phase two the model is extended to utilise real data from a corporate database of client information. The alife model highlighted needs for information not captured directly, requiring sophisticated inference techniques. Fuzzy logic is used to describe client behaviour with rules found through evolutionary optimisation. Analysis of mutual information between time series of clients investments is used to determine links between clients.

Original language	English
Title of host publication	Proc. 20th European Modeling and Simulation Symposium EMSS2008
Place of Publication	Switzerland?
Publisher	EMSS
Pages	1-9
Number of pages	9
Publication status	Published - 2008
Event	European Modeling and Simulation Symposium (EMSS) - Amantea, Calabria Duration: 17 Sep 2008 → 19 Sep 2008

Conference

Conference	European Modeling and Simulation Symposium (EMSS)
Period	17/09/08 → 19/09/08

Access to Document

CSU392264Accepted author manuscript, 450 KB

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Cite this

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title = "Simulation of Trust in Client: Wealth Management Advisor Relationships",

abstract = "This paper describes a two phase model for simulating trust amongst clients and their wealth management advisors. In phase one an artifcial life model was used to assess the dynamics of trust. In phase two the model is extended to utilise real data from a corporate database of client information. The alife model highlighted needs for information not captured directly, requiring sophisticated inference techniques. Fuzzy logic is used to describe client behaviour with rules found through evolutionary optimisation. Analysis of mutual information between time series of clients investments is used to determine links between clients.",

keywords = "Open access version available, Artificial Life, Relationships, Simulation, Trust, Wealth Management",

author = "Terence Bossomaier and Russell Standish",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = Switzerland?: EMSS, 2008. Event dates (773o) = 17th-19th September 2008; Parent title (773t) = European Modeling and Simulation Symposium (EMSS).; European Modeling and Simulation Symposium (EMSS) ; Conference date: 17-09-2008 Through 19-09-2008",

year = "2008",

language = "English",

pages = "1--9",

booktitle = "Proc. 20th European Modeling and Simulation Symposium EMSS2008",

publisher = "EMSS",

}

TY - GEN

T1 - Simulation of Trust in Client

T2 - European Modeling and Simulation Symposium (EMSS)

AU - Bossomaier, Terence

AU - Standish, Russell

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = Switzerland?: EMSS, 2008. Event dates (773o) = 17th-19th September 2008; Parent title (773t) = European Modeling and Simulation Symposium (EMSS).

PY - 2008

Y1 - 2008

N2 - This paper describes a two phase model for simulating trust amongst clients and their wealth management advisors. In phase one an artifcial life model was used to assess the dynamics of trust. In phase two the model is extended to utilise real data from a corporate database of client information. The alife model highlighted needs for information not captured directly, requiring sophisticated inference techniques. Fuzzy logic is used to describe client behaviour with rules found through evolutionary optimisation. Analysis of mutual information between time series of clients investments is used to determine links between clients.

AB - This paper describes a two phase model for simulating trust amongst clients and their wealth management advisors. In phase one an artifcial life model was used to assess the dynamics of trust. In phase two the model is extended to utilise real data from a corporate database of client information. The alife model highlighted needs for information not captured directly, requiring sophisticated inference techniques. Fuzzy logic is used to describe client behaviour with rules found through evolutionary optimisation. Analysis of mutual information between time series of clients investments is used to determine links between clients.

KW - Open access version available

KW - Artificial Life

KW - Relationships

KW - Simulation

KW - Trust

KW - Wealth Management

M3 - Conference paper

SP - 1

EP - 9

BT - Proc. 20th European Modeling and Simulation Symposium EMSS2008

PB - EMSS

CY - Switzerland?

Y2 - 17 September 2008 through 19 September 2008

ER -