People Efficiently Explore the Solution Space of the Computationally Intractable Traveling Salesman Problem to Find Near-Optimal Tours

Humans need to solve computationally intractable problems such as visual search, categorization, and simultaneous learning and acting, yet an increasing body of evidence suggests that their solutions to instantiations of these problems are near optimal. Computational complexity advances an explanation to this apparent paradox: (1) only a small portion of instances of such problems are actually hard, and (2) successful heuristics exploit structural properties of the typical instance to selectively improve parts that are likely to be sub-optimal. We hypothesize that these two ideas largely account for the good performance of humans on computationally hard problems. We tested part of this hypothesis by studying the solutions of 28 participants to 28 instances of the Euclidean Traveling Salesman Problem (TSP). Participants were provided feedback on the cost of their solutions and were allowed unlimited solution attempts (trials). We found a significant improvement between the first and last trials and that solutions are significantly different from random tours that follow the convex hull and do not have self-crossings. More importantly, we found that participants modified their current better solutions in such a way that edges belonging to the optimal solution (“good” edges) were significantly more likely to stay than other edges (“bad” edges), a hallmark of structural exploitation. We found, however, that more trials harmed the participants' ability to tell good from bad edges, suggesting that after too many trials the participants “ran out of ideas.” In sum, we provide the first demonstration of significant performance improvement on the TSP under repetition and feedback and evidence that human problem-solving may exploit the structure of hard problems paralleling behavior of state-of-the-art heuristics

Reserve design to optimize functional connectivity and animal density

Ecological distance-based spatial capture–recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget-constrained optimization problem and conducted a simulation to explore 3 different SCR-informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density-weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space-use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home-range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long-term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density-weighted connectivity or preemptively imposing home-range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high-density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home-range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.</p

Reserve design to optimize functional connectivity and animal density

Ecological distance-based spatial capture–recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget-constrained optimization problem and conducted a simulation to explore 3 different SCR-informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density-weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space-use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home-range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long-term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density-weighted connectivity or preemptively imposing home-range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high-density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home-range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.</p

Development Of A Building Information Modelling Asset (BIMAsset) Value Realisation Model

Building Information Modelling (BIM) is defined as a set of interacting processes, people and technologies that produce a methodology to digitally manage the data of a building, its performance, planning, construction, and later its operation. Although the business value of BIM has been observed during the design and construction processes, research efforts made towards documenting BIM business value when considering an asset’s lifecycle are insufficient. This PhD study is based on a three-part rationale: the lack of perceived business value of BIM; weakness in BIM business value measurement techniques; and barriers of BIM implementation in the lifecycle of assets. The aim of this research is to extend the knowledge base in relation to BIM business value realisation and BIM adoption in Asset Management (AM) by developing a model that will help to guide asset owners in realising business value of BIM implementation in an AM system throughout the lifecycle of built assets. Furthermore, the scope of the study is limited to the following: client as an asset owner; asset operations in relation to AM lifecycle; and BIM implementation during asset operations. The BIMAsset Value Realisation Model (BIMAsset VRM) is developed abductively from five sequential studies (Chapters 4-8). The BIMAsset VRM consists of six constituent elements; Influencers, Input, Output, Drivers (tangible and intangible value), Core and Business Value Realisation Dimension. Data obtained for this study is qualitative. Semi-structured interviews are used to collect data from 16 participants in 18 interviews in 6 case studies. Furthermore, documented data is sourced from these case studies in order to add depth to the study data. Data is collected in the UK, Finland, Denmark and the USA. These cases represent retail, government, health, education and consultant perspectives in AM. Data collected were analysed using mixed methods. Thematic analysis, content analysis, theoretical saturation and descriptive statistics are utilised in analysing and presenting the results of this study. The study utilised a six-member expert panel in the form of a focus group to validate the BIMAsset VRM. It is validated through expert opinion against pre-determined criteria of fruitfulness, prudence, quantification, scope, progressiveness, internal consistency and external consistency. The results of the focus group show that there is a majority opinion that the BIMAsset VRM satisfies the above validation criteria. Furthermore, in the data analysis, there is a majority opinion that the BAMM satisfies the above validation criteria. The study led to the following findings: (a) The study reveals that operational information requirements are strongly related to business needs and that there cannot be a rigid requirement list for all clients. (b) The study identifies that in order to successfully integrate BIM-AM systems, the asset owner should consider the following; the development for a clear strategy prior to adoption; connecting the strategy to the business goals; the discovery of organisational information needs for the development of Information Requirement templates. (c) The study establishes that there is real value to be derived by the asset owner from the effective management of asset information and there are six typologies of BIM business value that can be derived in AM; management, commerce, efficiency, industry, user and technology value. (d) The study identifies BIM strategy, contract management, lifecycle management, maintenance management, work-order management and value realisation management as activity systems that drive BIM business value in AM. (e) The study has discovered that the capability of asset owners to derive business value of BIM in AM has implementation and maturity implications. (f) The study suggests that intangible value can be tracked and measured through the activity of business value linkage using concept maps. (g) The study posits that the entire process of BIM business value realisation management is about driving change, measuring outcomes and continuous improvement of activity systems that drive value so as to validate predicted the desirable outcomes, discover the unpredicted desirable outcome, and eliminate both the predicted and unpredicted undesirable outcomes within the BIM-based AM system. The main scientific contribution of the study is the BIMAsset VRM which covers: (a) information requirement strategies; (b) BIM-AM systems integration techniques; (c) BIM business value realisation theory; (d) BIM-based information content management tools and techniques; (e) activity systems that drive BIM business value in AM; and (f) techniques for tracking and measuring tangible and intangible value in BIM-based processes. Similarly, the main practical contribution of this study is the deployment of BIMAsset VRM and BIMAsset Maturity Model (BAMM) in the form of a guide in an owner-operator organisation. The study discusses in great detail how an asset manager can plan, manage and implement the six dimensions of BIMAsset VRM in asset operations. Also, it presents a guide for tracking and measuring tangible and intangible BIM business value. These aspects ensure that the PhD thesis extends the current knowledge base and provides a cradle-to-grave approach to addressing the phenomenon of BIM business value realisation in an AM system during asset operations

Unifying reserve design strategies with graph theory and constraint programming

International audienceThe delineation of areas of high ecological or biodiversity value is a priority of any conservation program. However, the selection of optimal areas to be preserved necessarily results from a compromise between the complexity of ecological processes and managers’ constraints. Current reserve design models usually focus on few criteria, which often leads to an oversimplification of the underlying conservation issues. This paper shows that Constraint Programming (CP) can be the basis of a more unified, flexible and extensible framework. First, the reserve design problem is formalized. Secondly, the problem is modeled from two different angles by using two graph-based models. Then CP is used to aggregate those models through a unique Constraint Satisfaction Problem. Our model is finally evaluated on a real use case addressing the problem of rainforest fragmentation in New Caledonia, a biodiversity hotspot. Results are promising and highlight challenging perspectives to overtake in future work

An emergent approach to analogical inference

In recent years, a growing number of researchers have proposed that analogy is a core component of human cognition. According to the dominant theoretical viewpoint, analogical reasoning requires a specific suite of cognitive machinery, including explicitly coded symbolic representations and a mapping or binding mechanism that operates over these representations. Here we offer an alternative approach: we find that analogical inference can emerge naturally and spontaneously from a relatively simple, error-driven learning mechanism without the need to posit any additional analogy-specific machinery. The results also parallel findings from the developmental literature on analogy, demonstrating a shift from an initial reliance on surface feature similarity to the use of relational similarity later in training. Variants of the model allow us to consider and rule out alternative accounts of its performance. We conclude by discussing how these findings can potentially refine our understanding of the processes that are required to perform analogical inference