The Value of Imprecise Prediction

The traditional philosophy of science approach to prediction leaves little room for appreciating the value and potential of imprecise predictions. At best, they are considered a stepping stone to more precise predictions, while at worst they are viewed as detracting from the scientific quality of a discipline. The aim of this paper is to show that imprecise predictions are undervalued in philosophy of science. I review the conceptions of imprecise predictions, and the main criticisms levelled against them: (i) that they cannot aid in model selection and improvement and (ii) that they cannot support effective interventions in practical decision making. I will argue against both criticisms, showing that imprecise predictions have a circumscribed but important and legitimate place in the study of complex heterogeneous systems. The argument is illustrated and supported by an example from conservation biology, where imprecise models were instrumental in saving the kōkako from extinction

What is a target system?

Many phenomena in the natural world are complex, so scientists study them through simplified and idealised models. Philosophers of science have sought to explain how these models relate to the world. On most accounts, models do not represent the world directly, but through target systems. However, our knowledge of target systems is incomplete. First, what is the process by which target systems come about? Second, what types of entity are they? I argue that the basic conception of target systems, on which other conceptions depend, is as parts of the world. I outline the process of target system specification and show that it is a crucial step in modelling. I also develop an account of target system evaluation, based on aptness. Paying close attention to target system specification and evaluation can help scientists minimise the frequency and extent of mistakes, when they are using models to investigate phenomena in complex real-world systems.Peer reviewe

The Value of Imprecise Prediction

The traditional philosophy of science approach to prediction leaves little room for appreciating the value and potential of imprecise predictions. At best, they are considered a stepping stone to more precise predictions, while at worst they are viewed as detracting from the scientific quality of a discipline. The aim of this paper is to show that imprecise predictions are undervalued in philosophy of science. I review the conceptions of imprecise predictions and the main criticisms levelled against them: (i) that they cannot aid in model selection and improvement, and (ii) that they cannot support effective interventions in practical decision making. I will argue against both criticisms, showing that imprecise predictions have a circumscribed but important and legitimate place in the study of complex, heterogeneous systems. The argument is illustrated and supported by an example from conservation biology, where imprecise models were instrumental in saving the kōkako from extinction.Peer reviewe

Generality and Causal Interdependence in Ecology

A hallmark of ecological research is dealing with complexity in the systems under investigation. One strategy is to diminish this complexity by constructing models and theories that are general. Alternatively, ecologists can constrain the scope of their generalisations to particular phenomena or types of systems. However, research employing the second strategy is often met with scathing criticism. I offer a theoretical argument in support of moderate generalisations in ecological research, based on the notions of interdependence and causal heterogeneity and their effect on the tradeoff between generality and realis

The Value of Imprecise Prediction

The traditional philosophy of science approach to prediction leaves little room for appreciating the value and potential of imprecise predictions. At best, they are considered a stepping stone to more precise predictions, while at worst they are viewed as detracting from the scientific quality of a discipline. The aim of this paper is to show that imprecise predictions are undervalued in philosophy of science. I review the conceptions of imprecise predictions, and the main criticisms levelled against them: (i) that they cannot aid in model selection and improvement and (ii) that they cannot support effective interventions in practical decision making. I will argue against both criticisms, showing that imprecise predictions have a circumscribed but important and legitimate place in the study of complex heterogeneous systems. The argument is illustrated and supported by an example from conservation biology, where imprecise models were instrumental in saving the kōkako from extinction

What are general models about?

Models provide scientists with knowledge about target systems. An important group of models are those that are called general. However, what exactly is meant by generality in this context is somewhat unclear. The aim of this paper is to draw out a distinction between two notions of generality that has implications for scientific practice. Some models are general in the sense that they apply to many systems in the world and have many particular targets. Another sense is captured by models that are aimed at understanding the fundamental or underlying dynamics of a phenomenon, as opposed to how it manifests in each particular case. They have non-specific, i.e. generic targets. While both notions of generality and genericness are legitimate and correspond to different aspects of scientific practice, they must be distinguished. Failing to do so obscures the danger of overgeneralisation faced by general models and facilitates the illegitimate use of generic models as general models. This can lead to a reduction of the explanatory and predictive power of both

Abstract and Complete

There are two notions of abstraction that are often confused. The material view implies that the products of abstraction are not concrete. It is vulnerable to the criticism that abstracting introduces misrepresentations to the system, hence abstraction is indistinguishable from idealization. The omission view fares better against this criticism because it does not entail that abstract objects are non-physical and because it asserts that the way scientists abstract is different to the way they idealize. Moreover, the omission view better captures the way that abstraction is used in many parts of science. Disentangling the two notions is an important prerequisite for determining how to evaluate the use abstraction in science

The role of target systems in scientific practice

Scientists often construct simplified and idealized models in order to study complex phenomena. Yet they do not model a phenomenon in its entirety but target only the aspects of the phenomenon which they consider relevant. Hence, the model is said to describe the target system and not the whole phenomenon. The term `target system\u27 has become popular in the philosophy of science, yet most authors do not provide a definition or analysis of the concept. The result is that the term is used ambiguously, which has undermined its potential value and usefulness for scientific practice. The aim of this dissertation is to provide a cogent account of target systems and their importance in science, with examples taken from case studies in ecology. The central issue I explore in my dissertation concerns the nature of target systems. What are target systems? How are they specified? How can they be evaluated? In my dissertation I give an account of target systems as real parts of systems in the world, which are specified through a process of partitioning and abstraction. I also provide a tentative theory of target system evaluation based on the notion of aptness for a particular scientific purpose. A deep understanding of nature and function of targets can resolve problems in science. I use the term `target system analysis\u27, to denote the specification of target systems of one enquiry and the comparison of targets across enquiries. The last part of the dissertation is devoted to the application of the theory of target system specification and evaluation to a case study from actual scientific practice, invasion biology. Target system reveals that a scientist constructing a unificatory framework in invasion biology faces a tradeoff between generality and predictability. A truly unified framework must incorporate a multitude of different causes of invasion, yet the causes of each invasion are unique. Hence, invasion biology can have a unified theory, based on the process of invasion, yet this theory will be of little use to predicting particular invasions

What are general models about?

Elliott-Graves A. What are general models about? European Journal for Philosophy of Science . 2022;12(4): 74.Models provide scientists with knowledge about target systems. An important group of models are those that are called general. However, what exactly is meant by generality in this context is somewhat unclear. The aim of this paper is to draw out a distinction between two notions of generality that has implications for scientific practice. Some models are general in the sense that they apply to many systems in the world and have many particular targets. Another sense is captured by models that are aimed at understanding the fundamental or underlying dynamics of a phenomenon, as opposed to how it manifests in each particular case. They have non-specific, i.e. generic targets. While both notions of generality and genericness are legitimate and correspond to different aspects of scientific practice, they must be distinguished. Failing to do so obscures the danger of overgeneralisation faced by general models and facilitates the illegitimate use of generic models as general models. This can lead to a reduction of the explanatory and predictive power of both