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Abstract Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.
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A Framework for Computational Models of Human Memory
We present analysis of existing memory models, examining how models represent knowledge, structure memory, learn, make decisions, and predict reaction times. On the basis of this analysis, we propose a theoretical framework that characterizes memory modelling in terms of six key decisions: (1) choice of knowledge representation scheme, (2) choice of data structure, (3) choice of associative architecture, (4) choice of learning rule, (5) choice of time variant process, and (6) choice of response decision criteria. This framework is both descriptive and proscriptive: we intend to both describe the state of the literature and outline what we believe is the most fruitful space of possibilities for the development of future memory models.
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- PAR ID:
- 10067560
- Date Published:
- Journal Name:
- AAAI Fall Symposium, A Standard Model of Mind: AAAI Technical Report
- Issue:
- FS-17-05
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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