Understanding non-modular functionality – lessons from genetic algorithms

Evolution is often characterized as a tinkerer that creates efficient but messy solutions to problems. We analyze the nature of the problems that arise when we try to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem – solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show that the reason for why evolutionary designs are often hard to understand is that they exhibit non-modular functionality, and that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation

Understanding non-modular functionality – lessons from genetic algorithms

Evolution is often characterized as a tinkerer that creates efficient but messy solutions to problems. We analyze the nature of the problems that arise when we try to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem – solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show that the reason for why evolutionary designs are often hard to understand is that they exhibit non-modular functionality, and that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation

The Diversity-Ability Trade-Off in Scientific Problem Solving

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page (2004; see also Grim et al. 2019) is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model which captures the role of ‘ability’ in a meaningful way, and use it to explore the trade-offs between diversity and ability in scientific problem solving.Peer reviewe

Unification and mechanistic detail as drivers of model construction : Models of networks in economics and sociology

We examine the diversity of strategies of modelling networks in (micro) economics and (analytical) sociology. Field-specific conceptions of what explaining (with) networks amounts to or systematic preference for certain kinds of explanatory factors are not sufficient to account for differences in modelling methodologies. We argue that network models in both sociology and economics are abstract models of network mechanisms and that differences in their modelling strategies derive to a large extent from field-specific conceptions of the way in which a good model should be a general one. Whereas the economics models aim at unification, the sociological models aim at a set of mechanism schemas that are extrapolatable to the extent that the underlying psychological mechanisms are general. These conceptions of generality induce specific biases in mechanistic explanation and are related to different views of when knowledge from different fields should be seen as relevant. (C) 2014 Elsevier Ltd. All rights reserved.Peer reviewe

Explanatory relevance across disciplinary boundaries

Many of the arguments for neuroeconomics rely on mistaken assumptions about criteria of explanatory relevance across disciplinary boundaries and fail to distinguish between evidential and explanatory relevance. Building on recent philosophical work on mechanistic research programmes and the contrastive counterfactual theory of explanation, we argue that explaining an explanatory presupposition or providing a lower-level explanation does not necessarily constitute explanatory improvement. Neuroscientific findings have explanatory relevance only when they inform a causal and explanatory account of the psychology of human decision-making.Peer reviewe

Triangulation across the lab, the scanner and the field : the case of social preferences

Peer reviewe

The Diversity-Ability Trade-Off in Scientific Problem Solving

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page (2004; see also Grim et al. 2019) is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model which captures the role of ‘ability’ in a meaningful way, and use it to explore the trade-offs between diversity and ability in scientific problem solving.Peer reviewe

Society by Numbers : Studies on Model-Based Explanations in the Social Sciences

The aim of this dissertation is to provide conceptual tools for the social scientist for clarifying, evaluating and comparing explanations of social phenomena based on formal mathematical models. The focus is on relatively simple theoretical models and simulations, not statistical models. These studies apply a theory of explanation according to which explanation is about tracing objective relations of dependence, knowledge of which enables answers to contrastive why and how-questions. This theory is developed further by delineating criteria for evaluating competing explanations and by applying the theory to social scientific modelling practices and to the key concepts of equilibrium and mechanism. The dissertation is comprised of an introductory essay and six published original research articles. The main theses about model-based explanations in the social sciences argued for in the articles are the following. 1) The concept of explanatory power, often used to argue for the superiority of one explanation over another, compasses five dimensions which are partially independent and involve some systematic trade-offs. 2) All equilibrium explanations do not causally explain the obtaining of the end equilibrium state with the multiple possible initial states. Instead, they often constitutively explain the macro property of the system with the micro properties of the parts (together with their organization). 3) There is an important ambivalence in the concept mechanism used in many model-based explanations and this difference corresponds to a difference between two alternative research heuristics. 4) Whether unrealistic assumptions in a model (such as a rational choice model) are detrimental to an explanation provided by the model depends on whether the representation of the explanatory dependency in the model is itself dependent on the particular unrealistic assumptions. Thus evaluating whether a literally false assumption in a model is problematic requires specifying exactly what is supposed to be explained and by what. 5) The question of whether an explanatory relationship depends on particular false assumptions can be explored with the process of derivational robustness analysis and the importance of robustness analysis accounts for some of the puzzling features of the tradition of model-building in economics. 6) The fact that economists have been relatively reluctant to use true agent-based simulations to formulate explanations can partially be explained by the specific ideal of scientific understanding implicit in the practise of orthodox economics.Tämä väitöskirja tarjoaa käsitteellisiä välineitä ymmärtää ja arvioida teoreettisiin malleihin perustuvia yhteiskuntatieteellisiä selityksiä. Tutkimus nojaa näkemykseen, jonka mukaan selittämisessä on kyse sellaisten objektiivisten riippuvuussuhteiden jäljittämisestä, joita koskeva tieto mahdollistaa vastaamiseen miksi ja kuinka -kysymyksiin. Selityksiä voidaan lisäksi aina täsmentää määrittämällä selityksen kohteelle kontrasti. Tässä työssä tällaista selittämisen teoriaa kehitetään edelleen mm. erittelemällä kriteerejä, joilla selityksiä voidaan vertailla keskenään, ja soveltamalla teoriaa yhteiskuntatieteellisiin mallintamiskäytäntöihin sekä näille käytännöille keskeisiin tasapainon ja mekanismin käsitteisiin. Väitöskirja koostuu johdantoesseestä ja kuudesta julkaistusta tutkimusartikkelista. Artikkeleissa esitetyt keskeiset mallipohjaisia yhteiskuntatieteellisiä selityksiä koskevat väitteet ovat seuraavat: 1) Kilpailevia teorioita tai malleja vertaillaan usein selitysvoiman käsitteen avulla. Tätä epämääräistä käsitettä voidaan selventää erittelemällä selitysvoima viiteen osittain itsenäiseen ulottuvuuteen. 2) Kaikki tasapainoselitykset eivät ole syy seuraus -selityksiä, jotka selittävät tasapainotilan vallitsemista systeemin mahdollisilla alkutiloilla. Monet tasapainoselitykset ovat konstitutiivisia selityksiä, jotka selittävät systeemin makro-ominaisuuksia systeemin osien ja niiden keskinäisen organisaation avulla. 3) Mekanismin käsitettä käytetään mallipohjaisissa selityksissä kahdella eri tavalla, jotka ovat yhteydessä ratkaisevasti erilaisiin tutkimusheuristiikkoihin. 4) Epärealistiset mallinnusoletukset ovat selityksellisesti ongelmallisia vain jos mallin esittämä selittämisen kannalta keskeinen on riippuvainen kyseisistä oletuksista. Rationaalisen valinnan mallit eroavat usein tässä suhteessa toisistaan. Tästä syystä päätelmät rationaalisen valinnan teorian virheellisten oletusten seurauksista tulee tehdä mallikohtaisesti. 5) Robustiusanalyysi on menetelmä, jolla voidaan tutkia riippuuko selittävä riippuvuussuhde epätosista tai ongelmallisista mallinnusoletuksista. Robustiusanalyysin merkityksen tunnistaminen tekee eräistä ongelmallisilta vaikuttavista taloustieteellisen mallinnuskäytännön piirteistä ymmärrettäviä. 6) Taloustieteilijöiden nihkeä suhtautuminen teoreettisiin toimija-pohjaisiin simulaatiomalleihin selittyy osin taloustieteelliseen mallinnustapaan implisiittisesti sisältyvällä käsityksellä tieteellisen ymmärtämisen luonteesta

Evidential diversity and the triangulation of phenomena

Peer reviewe

How to be critical and realist about economics

Peer reviewe