Paulo Salles

Towards a structured approach to building qualitative reasoning models and simulations

Successful transfer and uptake of qualitative reasoning technology for modelling and simulation in a variety of domains has been hampered by the lack of a structured methodology to support formalisation of ideas. We present a framework that structures and supports the capture of conceptual knowledge about system behaviour using a qualitative reasoning approach. This framework defines a protocol for representing content that supports the development of a conceptual understanding of systems and how they behave. The framework supports modellers in two ways. First, it structures and explicates the work involved in building models. Second, it facilitates easier comparison and evaluation of intermediate and final results of modelling efforts. We show how this framework has been used in developing qualitative reasoning models about three case studies of sustainable development in different river systems.

Qualitative reasoning about population and community ecology

Traditional approaches to ecological modeling, based on mathematical equations, are hampered by the qualitative nature of ecological knowledge. In this article, we demonstrate that qualitative reasoning provides alternative and productive ways for ecologists to develop, organize, and implement models. We present a qualitative theory of population dynamics and use this theory to capture and simulate commonsense theories about population and community ecology. Advantages of this approach include the possibility of deriving relevant conclusions about ecological systems without numeric data; a compositional approach that enables the reusability of models representing partial behavior; the use of a rich vocabulary describing objects, situations, relations, and mechanisms of change; and the capability to provide causal interpretations of system behavior.

Qualitative models of interactions between two populations

Negative and positive interactions between populations such as competition, predator-prey and symbiosis, under the influence of environmental factors, have been pointed out as the main organising forces of communities. Due to explicit representation of knowledge and of causal relations, qualitative simulations can be useful for students to have insights on structure and behaviour of interacting populations. Our work is based on a library of model fragments representing basic population processes (such as natality and mortality), so that it is possible to derive complex community behaviour from ‘first principles’ [1]. Six types of interactions were modelled: neutralism (0,0), amensalism (0,−), commensalism (0,+), predator-prey (+,−), symbiosis (+,+) and competition (−,−). The symbols −, +, 0 represent, respectively, negative, positive or no effects from the other species. We define a ‘basic interaction model’, in which population1 produces some effect (effect1on2) that affects natality (born2) and mortality (dead2) of population2, while this one has an effect (effect2on1) which influences born1 and dead1. These influences may be positive or negative, according to the interaction type. For instance, in the predator-prey model effect1on2 has a positive influence on dead2 (increases mortality of prey) and effect2on1 has a negative influence on dead1 (decreases mortality of predator). Simulations with these models show typical behaviour of each interaction type, such as coexistence and competitive exclusion. The simulation models are organised according to educational learning routes, following the general scheme discussed in [2].

A qualitative reasoning model of algal bloom in the Danube Delta Biosphere Reserve (DDBR)

Abstract This paper presents a Qualitative Reasoning model of the algal bloom phenomenon and its effects in the Danube Delta Biosphere Reserve (DDBR) in Romania. Qualitative Reasoning models represent processes and their cause–effect relationships in a flexible and conceptually rich manner and as such can be used as instruments for capturing and sharing explanations of how systems behave. The DDBR model is based on expert knowledge and captures the main factors contributing to the dynamics of this aquatic ecosystem. Focal points of the model are the relationships between temperature, water pollution from the Danube catchment area, and cyanotoxins. These factors gravely affect aquatic biota and ultimately human wellbeing. In addition to capturing domain knowledge, this paper discusses solutions for representing typical patterns in ecological systems using Qualitative Reasoning techniques.

Representing and managing uncertainty in qualitative ecological models

Abstract Ecologists and decision makers need ways to understand systems, test ideas, and make predictions and explanations about systems. However, uncertainty about causes and effects of processes and parameter values is pervasive in models of ecological systems. Uncertainty associated with incomplete knowledge of a phenomenon–and incomplete knowledge of the limits of ones knowledge–is referred to as epistemic uncertainty. Here, we illustrate the use of qualitative reasoning (QR) as a modeling approach that supports simulation despite pervasive epistemic uncertainty in a system. We develop a QR model of a simple plant-resource system to illustrate how six sources of epistemic uncertainty can be expressed, assessed, and managed. These include uncertainty about system structure, quantity vagueness, functional relationships, unknown or exogenous processes, simulation outcomes, and post-diction or explanation of outcomes. We show that QR provides a useful framework for expressing uncertainty due to inexact knowledge about parameter values and for exploring the consequences of different understandings of system structure. Furthermore, uncertainty in parameter values can be expressed and managed using different representations of and constraints on parameter quantity spaces. Compositional modeling supports the creation of alternative models representing different system structures. QR models allow the creation of a full envisionment of all possible outcomes given a set of causal processes, a particular system structure, and starting values. Finally, explicit representation of system structure and causality allows simulation results to be unambiguously explained. These features of QR support ecologists in making explicit their substantial qualitative knowledge about causes and effects in systems to produce models that give rise to insightful simulations of system dynamics.

Evaluating the potential of qualitative reasoning models to contribute to sustainable catchment management

Due to the world wide degradation of river catchments and their related aquatic resources the development of integrated management strategies has become an important issue. Tools and processes are required that support the integration of science, the needs of stakeholders and the local population, within existing political frameworks to achieve a sustainable catchment development. In this paper the potential of qualitative reasoning (QR) models for sustainable catchment management is evaluated by students and domain experts. This evaluation yields promising results. The evaluated QR models were found to represent complex knowledge in an understandable manner. Most people ‘largely or fully agreed’ that the presented QR models may significantly contribute to the understanding of students and stakeholders, concerning which entities and processes drive a sustainable development of a riverine landscape, and therefore enhances their decision-making capabilities. Due to its potential to integrate quantitative and qualitative knowledge, to build causal models, and to run dynamic simulations, the presented QR approach has great potential to become an important contribution to integrated catchment management at multiple levels of the implementation process thereof (such as education, decision-making, social learning, integration of different scientific disciplines, and communication).

Learning spaces as representational scaffolds for learning conceptual knowledge of system behaviour

Scaffolding is a well-known approach to bridge the gap between novice and expert capabilities in a discovery-oriented learning environment. This paper discusses a set of knowledge representations referred to as Learning Spaces (LSs) that can be used to support learners in acquiring conceptual knowledge of system behaviour. The LSs are logically self-contained, meaning that models created at a specific LS can be simulated. Working with the LSs provides scaffolding for learners in two ways. First, each LS provides a restricted set of representational primitives to express knowledge, which focus the learners knowledge construction process. Second, the logical consequences of an expression derived upon simulating, provide learners a reflective instrument for evaluating the status of their understanding, to which they can react accordingly. The work presented here is part of the DynaLearn project, which builds an Interactive Learning Environment to study a constructive approach to having learners develop a qualitative understanding of how systems behave. The work presented here thus focuses on tools to support educational research. Consequently, user-oriented evaluation of these tools is not a part of this paper.

A qualitative model of limiting factors for a salmon life cycle in the context of river rehabilitation

Qualitative Reasoning modelling has been promoted as a tool for formalising, integrating and exploring conceptual knowledge in ecological systems, such as river rehabilitation, which draw different information from multiple domains. A qualitative model was developed in Garp3 to capture and formalise knowledge about river rehabilitation and the management of an Atlantic salmon population, for use in an educational setting. The model integrated information about the ecology of the salmon life cycle, the environmental factors that may limit the survival of key life stages and their links with human activities such as agriculture, habitat rehabilitation and fishing. Whilst the compositional approach to qualitative modelling allowed simple representation of component concepts, the successful integration of these components in simulations of complex scenarios required a number of abstract representations, assumptions and technical modelling solutions to handle aspects of ambiguity and complexity, and to obtain the desired system behaviour. The final scenarios and simulations produced were able to represent river rehabilitation concepts in the context of a complete life cycle, but at this scale processing the simulations was very time consuming. Therefore, to handle this complexity an additional series of smaller demonstrator scenarios was developed that succinctly explored individual concepts within the system. This study indicates that qualitative modelling may be a valuable tool for exploring large systems, provided suitable means can be used to handle complexity and ambiguity.

The river Mesta case study: A qualitative model of dissolved oxygen in aquatic ecosystems

The dynamics of the dissolved oxygen in water bodies is the result of complex interactions involving physical and biological processes. Understanding how the balance of these influences determines the amount of oxygen available for living organisms is a key factor to interpret the water body conditions, and eventually to use dissolved oxygen as an indicator of the water quality. In this paper we present a Qualitative Reasoning model developed to improve understanding of changes in the amount of dissolved oxygen in different segments of the river Mesta in Bulgaria. Effects on dissolved oxygen result from changes in physical, chemical and biological processes induced both by natural and anthropogenic activities within the watershed. To explore the possibility of establishing a landmark value that may change according to specific conditions, we developed the concept of flexible value mapping, which dynamically captures changes in the dependencies between the landmark value and the values of other quantities as the conditions of the system change during the simulations. The paper also discusses the concept of dominance of a specific process over other competing processes affecting a quantity. With the model described here, we aim to discuss possible solutions to interesting modelling problems and to provide the community of ecological modellers support for educational activities and water resources management.

DynaLearn: architecture and approach for investigating conceptual system knowledge acquisition

DynaLearn is an Interactive Learning Environment that facilitates a constructive approach to developing a conceptual understanding of how systems work The software can be put in different interactive modes facilitating alternative learning experiences, and as such provides a toolkit for educational research.