TRACE: A Dynamic Model of Trust for People-Driven Service Engagements Combining Trust with Risk, Commitments, and Emotions Anup K. Kalia Advisor: Munindar P . Singh Department of Computer Science North Carolina State University Raleigh, NC 27695, USA September 30, 2015 Anup Kalia (NCSU) TRACE September 30, 2015 1 / 24
Broader Objectives ◮ Understand subtle human and organizational relationships ◮ Use such relationships as a basis for estimating trust ��������� ��������� ������������ �������� ����������� ����� ���� ����� ����������� Anup Kalia (NCSU) TRACE September 30, 2015 2 / 24
Research Question ◮ How to estimate trust between people from their interactions? Possible Applications ◮ Support people to make important decisions in organizational settings ◮ Estimating team cohesion or performance Anup Kalia (NCSU) TRACE September 30, 2015 3 / 24
Limitations With Existing Approaches Several approaches consider commitments alone for trust estimation. ◮ Gambetta (1988) interprets trust as a truster’s assessment of a trustee for performing a specific task ◮ Mayer et al. (1995) define trust as the willingness of a truster to be vulnerable to a trustee for the completion of a task ◮ Teacy et al. (2006) consider trust as the truster’s estimation of probability that a truster will fulfill it’s obligation toward a trustee ◮ Wang et al. (2011) represent trust as the belief of a truster that trustee will cooperate. They estimate trust by aggregating positive and negative experiences ◮ Kalia et al. (2014) consider commitment outcomes to predict trust where they learn truster’s parameters based on whether outcomes are positive, negative, or neutral Anup Kalia (NCSU) TRACE September 30, 2015 4 / 24
Limitations With Existing Approaches Two major classes of trust models ◮ Fixed parameter trust models where parameter are manually fixed ◮ Machine-learned trust models typically Hidden Markov Models (HMM) that assumes variables are conditionally independent of each other given the output variable Anup Kalia (NCSU) TRACE September 30, 2015 5 / 24
Proposed Approach We can improve trust prediction by incorporating (in addition to commitments) two attributes ◮ Risk taken by a truster toward a trustee ◮ Risk taken depends on a truster’s belief about the likelihood of gains or losses it might incur from its relationships with a trustee ◮ Emotions displayed by a truster toward a trustee ◮ Studies in psychology suggest that positive emotions increase trust whereas negative emotions decrease trust ◮ Create TRACE a model based on Conditional Random Field (CRF) ◮ Conditional independences between risk, commitments, and emotions may not hold in our setting (e.g., in HMM) Anup Kalia (NCSU) TRACE September 30, 2015 6 / 24
Background: Commitment & Trust ������������������������ ������� ��������� ��������� ����������� ������������������� ���������������� �������������������� ��������������� �� ��������������������������� �������������������� ��������������� Anup Kalia (NCSU) TRACE September 30, 2015 7 / 24
Background: Commitment Lifecycle ◮ C(Debtor, Creditor, Antecedent, Consequent) null create expire ������ conditional detached ���������� cancel consequent cancel terminated discharged violated Anup Kalia (NCSU) TRACE September 30, 2015 8 / 24
Background: Estimating Trust from Commitment Progression ◮ Two-valued representation, positive and negative experiences: � r , s � r ◮ Trust α = r + s ◮ We characterize each subject via four parameters ◮ Initial values, � r in , s in � ◮ Increment for positive and negative experiences: i r and i s Trust � r fi , s fi � Commitment Operation Commissive create � λ i r + r in , (1- λ ) i s + s in � Directive create Delegate None � i r + r in , s in � Discharge � r in , i s + s in � Cancel Anup Kalia (NCSU) TRACE September 30, 2015 9 / 24
Trust Antecedent Framework (Mayer et al., 1995) We propose TRACE based on the enhance trust antecedent framework ������� ����������� ����� ����������� ���� ��������� �������� ◮ The model contains 4 variables trust (T), risk (R), commitments (C), and emotions (E) ◮ Each variable V = � T, R, C, E � is described using using Singh’s (1999, 2011) formal notation V � debtor , creditor , antecedent , consequent � Anup Kalia (NCSU) TRACE September 30, 2015 10 / 24
Description of Variables ◮ C � trustee , truster , antecedent , consequent � ◮ The trustee commits to the truster to perform the consequent ◮ If the trustee performs the consequent, the commitment is satisfied ◮ R � truster , trustee , antecedent , consequent � ◮ The truster takes a risk by accepting the trustee’s offer to perform the consequent ◮ If the trustee performs the consequent, the truster gains ◮ T � truster , trustee , antecedent , consequent � ◮ The truster believes the trustee if the trustee performs the consequent ◮ Trust has three dimensions: ability (trustee’s competency), benevolence (trustee’s willingness), integrity (trustee’s ethics and morality) ◮ E � truster , trustee , antecedent , consequent � ◮ The truster displays a positive emotion if the trustee performs the consequent Anup Kalia (NCSU) TRACE September 30, 2015 11 / 24
Postulates We propose postulates that capture relationships between the variables P 1 : T t → T t + 1 . The trust T t + 1 is influenced by the past trust T t P 2 : C t → T t . The current commitment outcome C t influences the current trust T t P 3 : R t → C t . The risk taken influences the commitment outcome C t or the gain or loss realized in the risk R t P 4 : R t → T t . The current risk taken R t influences the current trust T t P 5 : C t → E t . The commitment outcome C t influences the current emotion E t P 6 : R t → E t . The risk taken R t influences the truster’s emotion E t P 7 : E t → T t . The current emotion E t influences the current trust T t Anup Kalia (NCSU) TRACE September 30, 2015 12 / 24
The TRACE Model Graphical representation of HMM and TRACE trust models (two time slices) ��� � � � ��� � � � � � � ��� ��� ��� � � � ����� � � � ��� � � � � � � ��� ��� ��� � � � Anup Kalia (NCSU) TRACE September 30, 2015 13 / 24
Comparing HMM and CRF ◮ HMM makes two independent assumptions ◮ The current state y t is independent of y 1 , y 2 , . . . , y t − 2 , given y t − 1 ◮ Observations x t are independent of each other, given y t ◮ CRF ◮ CRFs are agnostic to dependencies between the observations ◮ CRF model employs discriminative modeling, where the distribution p ( � y | � x ) is learned directly from the data Anup Kalia (NCSU) TRACE September 30, 2015 14 / 24
Evaluation We evaluate TRACE via data collected from a human-subject study conducted by the Intelligence Advanced Research Projects Activity (IARPA) ◮ IARPA prepared a dataset based on the Checkmate protocol adapted from the investment or dictator economic decision-making game (Berg, 1995) ◮ The data consists of 431 rows collected from 63 subjects ◮ Each row corresponds to the sequence of rounds played between two subjects ◮ The data we obtained reflects only the banker’s perspective ������� ������� ��������� ����������� ��������� ���������� �������� ��������������������� ����������� ������ ������ ��������������������� ����������� ��������������������� ������ ����������� �������� ��������������������� ����������� ������ ������� ������� ��������� ����������� ��������� ���������� ��������������������� ����������� ������ �������� ��������������������� ������ ����������� ��������������������� ������ ����������� �������� ��������������������� ����������� ������ Anup Kalia (NCSU) TRACE September 30, 2015 15 / 24
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