Acquiring symbolic design optimization problem reformulation knowledge: On computable relationships between design syntax and semantics

This thesis presents a computational method for the inductive inference of explicit and implicit semantic design knowledge from the symbolic-mathematical syntax of design formulations using an unsupervised pattern recognition and extraction approach. Existing research shows that AI / machine learning based design computation approaches either require high levels of knowledge engineering or large training databases to acquire problem reformulation knowledge. The method presented in this thesis addresses these methodological limitations. The thesis develops, tests, and evaluates ways in which the method may be employed for design problem reformulation. The method is based on the linear algebra based factorization method Singular Value Decomposition (SVD), dimensionality reduction and similarity measurement through unsupervised clustering. The method calculates linear approximations of the associative patterns of symbol cooccurrences in a design problem representation to infer induced coupling strengths between variables, constraints and system components. Unsupervised clustering of these approximations is used to identify useful reformulations. These two components of the method automate a range of reformulation tasks that have traditionally required different solution algorithms. Example reformulation tasks that it performs include selection of linked design variables, parameters and constraints, design decomposition, modularity and integrative systems analysis, heuristically aiding design “case” identification, topology modeling and layout planning. The relationship between the syntax of design representation and the encoded semantic meaning is an open design theory research question. Based on the results of the method, the thesis presents a set of theoretical postulates on computable relationships between design syntax and semantics. The postulates relate the performance of the method with empirical findings and theoretical insights provided by cognitive neuroscience and cognitive science on how the human mind engages in symbol processing and the resulting capacities inherent in symbolic representational systems to encode “meaning”. The performance of the method suggests that semantic “meaning” is a higher order, global phenomenon that lies distributed in the design representation in explicit and implicit ways. A one-to-one local mapping between a design symbol and its meaning, a largely prevalent approach adopted by many AI and learning algorithms, may not be sufficient to capture and represent this meaning. By changing the theoretical standpoint on how a “symbol” is defined in design representations, it was possible to use a simple set of mathematical ideas to perform unsupervised inductive inference of knowledge in a knowledge-lean and training-lean manner, for a knowledge domain that traditionally relies on “giving” the system complex design domain and task knowledge for performing the same set of tasks

Working Paper: Measuring polycentricity via network flows, spatial interaction, and percolation

Polycentricity is most commonly measured by location-based metrics (e.g. employment density or total number of workers, above a threshold, used to count the number of centres). While these metrics are good indicators of location ‘centricity’, the results are sensitive to threshold-choice. We consider here the alternate idea that a centre’s status depends on which other locations it is con- nected to in terms of trip inflows and outflows: this is inherently a network rather than a location idea. A set of flow and network-based centricity metrics for measuring metropolitan area poly- centricity using Journey-To-Work (JTW) data are presented: (a) trip-based, (b) density-based, and, (c) accessibility-based. Using these measures, polycentricity is computed and rank-centricity distributions are plotted to test whether these distributions follow Zipf-like or Chirstaller-like distributions. Further, a percolation theory framework is proposed for the full origin-destination (OD) matrix, where trip flows are used as a thresholding parameter to count the number of sub-centres. It is found that trip flows prove to be an effective measure to count and hierarchically organise metropolitan area sub-centres, and provide one way of dealing with the arbitrariness of defining a threshold on numbers of employed persons, employment density, or centricities to count sub-centres. These measures demonstrated on data from the Greater Sydney region show that the trip flow-based threshold and network centricities help to characterize polycentricity more robustly than the traditional number or density-based thresholds alone and provide unexpected insights into the connections between land use, transport, and urban structure

Evidence for localization and urbanization economies in urban scaling

We study the scaling of (i) numbers of workers and aggregate incomes by occupational categories against city size, and (ii) total incomes against numbers of workers in different occupations, across the functional metropolitan areas of Australia and the US. The number of workers and aggregate incomes in specific high income knowledge economy related occupations and industries show increasing returns to scale by city size, showing that localization economies within particular industries account for superlinear effects. However, when total urban area incomes and/or Gross Domestic Products are regressed using a generalised Cobb-Douglas function against the number of workers in different occupations as labour inputs, constant returns to scale in productivity against city size are observed. This implies that the urbanization economies at the whole city level show linear scaling or constant returns to scale. Furthermore, industrial and occupational organisations, not population size, largely explain the observed productivity variable. The results show that some very specific industries and occupations contribute to the observed overall superlinearity. The findings suggest that it is not just size but also that it is the diversity of specific intra-city organization of economic and social activity and physical infrastructure that should be used to understand urban scaling behaviors.Comment: 17 pages, 3 table

The role of analytical models and their circulation in urban studies and policy

Cities are so complex that we constantly build models to represent them, understand them and attempt to plan them. Models represent a middle ground between the singular configurations of cities and universal theories. This is what makes them valuable and prone to circulate (between places, institutions and languages) and evolve to adapt to new ideas, local conditions and/or other models. When it comes to analytical urban models (i.e. analytical representations of cities developed to study or simulate part of their structure or dynamics), there is a lack of academic understanding regarding how context and circulation affect their content, use and interpretation. What happens to analytical urban models and their reception during their circulation across geographical and disciplinary boundaries? How have different academic disciplines interacted with, contributed to and been influenced by analytical urban models? What are the consequences of urban models’ mobility for our understanding of cities? In this article, we employ the policy mobilities framework to analyse the circulation of analytical urban models. We use six canonical models as case studies to determine how their assumptions came about and how these models have circulated across different domains of policy and application by using biographical information and model analysis. The first contribution of the article is to demonstrate by example that our hypothesis regarding the influence of context is consistent. We also show that highly transferable/mobile models share common characteristics relating to contingent factors such as their creators’ biographies, institutional context and the traditional markers of power relations

A linguistic approach to assess the dynamics of design team preference in concept selection

This paper addresses the problem of describing the decision-making process of a committee of engineers based upon their verbalized linguistic appraisals of alternatives. First, we show a way to model an individual’s evaluation of an alternative through natural language based on the Systemic-Functional Linguistics system of APPRAISAL. The linguistic model accounts for both the degree of intensity and the uncertainty of expressed evaluations. Second, this multi-dimensional linguistic model is converted into a scalar to represent the degree of intensity and a probability distribution function for the stated evaluation. Finally, we present a Markovian model to calculate the time-varying change in preferential probability, the probability that an alternative is the most preferred alternative. We further demonstrate how preferential probability toward attributes of alternatives correspond to preferential probability toward alternatives. We illustrate the method on two case studies to highlight the time-variant dynamics of preferences toward alternatives and attributes. This research contributes to process tracing in descriptive decision science to understand how engineers actually take decisions.National Science Foundation (U.S.) (Award CMMI-0900255