Modeling complexity: cognitive constraints and computational model-building in integrative systems biology

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations

Model of Learning Ability

The problem domain of the investigation presented in this dissertation is knowledge increase. In particular the research is concerned with the process of knowledge increase. The research problem formulated is formulated a posteriori: "Which factors determine the increase of personal knowledge that occurs by absorbing a particular new knowledge of an individual, who is a member of an organization, and how these factors work?" To explore and shed light on this problem a number of disciplinary boundaries were engaged and some models, tools, descriptions, etc. were borrowed from a number of related disciplines. These areas are briefly presented in the dissertation, restricting presentation to the relevant issues. There are three models developed for this thesis and they are subsequently integrated into a fourth model. First the 'Model of Learning Willingness' (MLW) is developed to consider personal and organizational value systems. For this model, new concepts have been created, to indicate the position of new knowledge in both personal and organizational value systems. Stable and the unstable states of the model are identified as well as how it is possible to pass from one state to another as result of an interaction between the two value systems by means of influencing each other. Applying a 'systems theory approach' on the cognitive psychology conception of knowledge, the impact of the characteristics of existing knowledge on the absorption of new knowledge is described. The developed model is called the 'Model of Learning Capability' (MLC). - This is the second model. It is also necessary to pay attention to the ability to acquire new knowledge; this is described by the 'Model of Attention' (MA) - the third model. This model is based on two main factors, namely cognitive and social conditions. These three models are thus integrated into fourth one, which is called the 'Model of Learning Ability' (MLA). For exploration/validation the model is wwwed with the Doctus Knowledge-Based Expert System, which was also the means of comparing the evolved hypotheses with the input from reality, namely observations and thought experiments. The first insight from the model is a better understanding of the process of 'knowledge increase'. The model can also be used to support choosing the right person to learn a particular piece of new knowledge, to identify the reason for someone not performing well with regards to learning and/or identifying a possible way of improving the process. Using the logic of the model experts can also be evaluated in the process of knowledge acquisition when building an expert system. Considering the achieved results some new problems emerge: It is not known what motivates the personal value system during the knowledge absorption; it is not known if the model can be extended to other forms of knowledge increase besides learning; it is not known how the social factors apart from love (i.e. power and money) affect the attention. Some new research ideas also evolved from this investigation, e.g. an attempt to model the knowledge using dimensions of understanding

Cardiogram of the park: quantitative analysis of walking scenarios of Trakų Vokė historic park

In this research, the concept of isovist [1,6] was employed to analyze spaces of the park as a container and catalyser of human activities and experiences in quantitative terms. The concept of the isovist defines the visual environment as a system of objects that structures the light as a source of stimuli for human perception. Trakų Vokė ensemble was selected as a case study object to test this quantitative approach towards historic park analysis. Methods of the research include a literature review on specific characteristics of Trakų Vokė ensemble, observation on site, analysis of available maps and satellite images, development of linear drawing of the park using AutoCAD, modelling using Isovist_App and ESRI ArcMap software, analysis, and discussion of results. The research has demonstrated that the results of the isovist and visual graph-based analysis reflect the observed spatial features of Trakų Vokė Park quite well and can be used for various purposes, including a more detailed description of valuable features of heritage objects, a detailed comparison between different parks, simulative reconstruction of the character of the historical park in the past based on historical data, maintenance and management of the park, parametric design of landscape spaces, etc

An investigation into the relationship between fiction and nonfiction reading exposure, and factors of critical thinking

Reading fiction has been associated with improved social and imaginative reasoning that could lead to improved critical thinking. This observational study investigated the relationship between fiction and nonfiction exposure, narrative transportation, and factors of critical thinking (critical thinking disposition, and epistemological orientation). Self-selecting participants (N = 335) completed an online survey including an author recognition test and self-report scales. Fiction scores were significantly associated with higher critical thinking disposition, while nonfiction had an inverse effect correlating with lower disposition. Fiction reading was associated with decreased absolutism, and nonfiction score conversely with higher absolutism. Total and nonfiction print exposure were associated with lower multiplism, with no significant association for fiction. Total and fiction print exposure were associated with higher evaluativism, with no significant association for nonfiction. Narrative transportation mediated some of these relationships. These findings provide a basis for further research into reading fiction and nonfiction, and critical thinking

Abstraction, Imagery, and Control in Cognitive Architecture.

This dissertation presents a theory describing the components of a cognitive architecture supporting intelligent behavior in spatial tasks. In this theory, an abstract symbolic representation serves as the basis for decisions. As a means to support abstract decision-making, imagery processes are also present. Here, a concrete (highly detailed) representation of the state of the problem is maintained in parallel with the abstract representation. Perceptual and action systems are decomposed into parts that operate between the environment and the concrete representation, and parts that operate between the concrete and abstract representations. Control processes can issue actions as a continuous function of information in the concrete representation, and actions can be simulated (imagined) in terms of it. The agent can then derive useful abstract information by applying perceptual processes to the resulting concrete state. This theory addresses two challenges in architecture design that arise due to the diversity and complexity of spatial tasks that an intelligent agent must address. The perceptual abstraction problem results from the difficulty of creating a single perception system able to induce appropriate abstract representations in each of the many tasks an agent might encounter, and the irreducibility problem arises because some tasks are resistant to being abstracted at all. Imagery works to mitigate the perceptual abstraction problem by allowing a given perception system to work in more tasks, as perception can be dynamically combined with imagery. Continuous control, and the simulation thereof via imagery, works to mitigate the irreducibility problem. The use of imagery to address these challenges differs from other approaches in AI, where imagery is considered as an alternative to abstract representation, rather than as a means to it. A detailed implementation of the theory is described, which is an extension of the Soar cognitive architecture. Agents instantiated in this architecture are demonstrated, including agents that use reinforcement learning and imagery to play arcade games, and an agent that performs sampling-based motion planning for a car-like vehicle. The performance of these agents is discussed in the context of the underlying architectural theory. Connections between this work and psychological theories of mental imagery are also discussed.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78795/1/swinterm_1.pd

Designing a BDI agent reactant model of behavioural change intervention

Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state. The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions.Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioral change interventions. Nevertheless, existing BDI agent models do not represent agent’s behavioral factors explicitly. This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioral change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioral change and psychological theories.Also, using mathematical analysis the model was evaluated. The objective of the proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions.The model will be realized in an agent based application that motivates children towards oral hygiene. The study explicitly depicts how agent’s behavioral factors interact to enhance behavior change which will assist agent-based intervention designers to be able to design intervention that will be void of reactance