We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. We also claim that (...) our account could be fruitfully used in appraising other models in philosophy and science. (shrink)

It has recently been argued that successful evidence-based policy should rely on two kinds of evidence: statistical and mechanistic. The former is held to be evidence that a policy brings about the desired outcome, and the latter concerns how it does so. Although agreeing with the spirit of this proposal, we argue that the underlying conception of mechanistic evidence as evidence that is different in kind from correlational, difference-making or statistical evidence, does not correctly capture the role that information about (...) mechanisms should play in evidence-based policy. We offer an alternative account of mechanistic evidence as information concerning the causal pathway connecting the policy intervention to its outcome. Not only can this be analyzed as evidence of difference-making, it is also to be found at any level and is obtainable by a broad range of methods, both experimental and observational. Using behavioral policy as an illustration, we draw the implications of this revised understanding of mechanistic evidence for debates concerning policy extrapolation, evidence hierarchies, and evidence integration. (shrink)

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 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 that captures the role of ‘ability’ in a meaningful way, and we use it to explore the trade-offs between diversity and (...) ability in scientific problem solving. (shrink)

In this introduction to the Topical Collection on Social and Cognitive Diversity in Science, we map the questions that have guided social epistemological approaches to diversity in science. Both social and cognitive diversity of different types is claimed to be epistemically beneficial. The challenge is to understand how an increase in a group’s diversity can bring about epistemic benefits and whether there are limits beyond which diversity can no longer improve a group’s epistemic performance. The contributions to the Topical Collection (...) discuss various proposals to maintain an appropriate amount of cognitive diversity in science, for instance, by recruiting and retaining practitioners from underrepresented social groups, providing incentives for explorative and risky research, encouraging interdisciplinary collaborations and stakeholder participation in research, requiring industry scientists to share their evidence, and developing strategies to encounter politically motivated attempts to manufacture doubt. To be successful, efforts to promote diversity in science should anticipate risks related to institutional interventions, navigate trade-offs between different types of epistemically good outcomes, and identify hidden costs that such policies may cause for various actors. Such efforts need to be assessed not only from an epistemic perspective but also from the point of view of fairness and the political legitimacy of scientific institutions. (shrink)

We review the most prominent modeling approaches in social epistemology aimed at understand- ing the functioning of epistemic communities and provide a philosophy of science perspective on the use and interpretation of such simple toy models, thereby suggesting how they could be integrated with conceptual and empirical work. We highlight the need for better integration of such models with relevant findings from disciplines such as social psychology and organization studies.

Real kinds, both natural and social categories, are characterized by rich inductive potential. They have relatively stable sets of conceptually independent projectable properties. Somewhat surprisingly, even some purely social categories show such multiple projectability. The article explores the origin of the inductive richness of social categories and concepts. I argue that existing philosophical accounts provide only a partial explanation, and mechanisms of boundary formation and stabilization must be brought into view for a more comprehensive account of inductively rich social categories.

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 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. (shrink)

Interdisciplinarity is strongly promoted in science policy across the world. It is seen as a necessary condition for providing practical solutions to many pressing complex problems for which no single disciplinary approach is adequate alone. In this article we model multi- and interdisciplinary research as an instance of collective problem solving. Our goal is to provide a basic representation of this type of problem solving and chart the epistemic benefits and costs of researchers engaging in different forms of cognitive coordination. (...) Our findings suggest that typical forms of interdisciplinary collaboration are unlikely to find optimal solutions to complex problems within short time frames and can lead to methodological conservatism. This provides some grounds for both reflecting on current science policy and envisioning more effective scientific practices with respect to interdisciplinary problem solving. (shrink)

Universities are knowledge institutions. Compared to several other knowledge institutions (e.g. schools, government research organisations, think tanks), research universities have unusual, anarchic organisational features. We argue that such anarchic features are not a weakness. Rather, they reflect the special standing of research universities among knowledge institutions. We contend that the distributed, self-organising mode of knowledge production maintains a diversity of approaches, topics and solutions needed in frontier research, which involves generating relevant knowledge under uncertainty. Organisational disunity and inconsistencies should sometimes (...) be protected by institutional structures and procedures in order for research universities to best serve their purpose as knowledge institutions. The quality control for the knowledge produced stems from knowledge fields, clusters of knowledge and research that exist beyond the confines of individual organisations. The diversity of epistemic contributions is therefore kept in check by the order imposed by the internal logic of science as a social practice. Our argument provides a new defence for the autonomy of research conducted at universities. (shrink)

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.

Ian Hacking (born in 1936, Vancouver, British Columbia) is most well-known for his work in the philosophy of the natural and social sciences, but his contributions to philosophy are broad, spanning many areas and traditions. In his detailed case studies of the development of probabilistic and statistical reasoning, Hacking pioneered the naturalistic approach in the philosophy of science. Hacking’s research on social constructionism, transient mental illnesses, and the looping effect of the human kinds make use of historical materials to shed (...) light on how developments in the social, medical, and behavioural sciences have shaped our contemporary conceptions of identity and agency. Hacking’s other contributions to philosophy include his work on the philosophy of mathematics (Hacking, 2014), philosophy of statistics, philosophy of logic, inductive logic (Hacking, 1965, 1979, 2001) and natural kinds (Hacking, 1991, 2007a). (shrink)

Human behavior is not always independent of the ways in which humans are scientifically classified. That there are looping effects of human kinds has been used as an argument for the methodological separation of the natural and the human sciences and to justify social constructionist claims. We suggest that these arguments rely on false presuppositions and present a mechanisms-based account of looping that provides a better way to understand the phenomenon and its theoretical and philosophical implications.

Tarkastelemme tässä artikkelissa, kuinka ihmisen psykologinen taipumus olemusajatteluun eli niin kutsuttu psykologinen essentialismi voisi näkyä argumentaatiossa. Esittelemme ensin psykologista tutkimusta aiheesta, minkä jälkeen tarkastelemme ilmiön merkitystä argumentaation ja sen tutkimuksen kannalta. Olemusajattelu näkyy julkilausumattomina taustaoletuksina, jotka kuitenkin vaikuttavat ihmisten tapaan tehdä päätelmiä ja rakentaa argumentteja. Argumentaation yhteydessä olemusajattelua tulee tarkastella taipumuksena tietynlaisiin piilopremisseihin. Lopuksi pohdimme, mitä merkitystä tällä voisi olla filosofian näkökulmasta.

Tässä luvussa tarkastelemme hypoteesien testaamista ja kokeellista kausaalista järkeilyä tieteenfilosofisesta näkökulmasta. Arvioimme kokeellisen menetelmän mahdollisuuksia ja rajoituksia yhteiskuntatieteellisen tutkimuksen kontekstissa, jossa luonnontieteille ominaisia yleispäteviä teorioita harvoin on saatavilla ja jossa suoraviivaisiin kausaaliväitteisiin suhtaudutaan usein epäillen. Tämä luku ei siis ole menetelmäopas, joka kädestä pitäen opastaisi, kuinka yhteiskuntatieteellisiä kokeita tulisi rakentaa, vaan katsaus niihin perustaviin metodologisiin kysymyksiin ja periaatteisiin, joihin varsinaiset menetelmät nojaavat.

Nudge and boost are two competing approaches to applying the psychology of reasoning and decision making to improve policy. Whereas nudges rely on manipulation of choice architecture to steer people towards better choices, the objective of boosts is to develop good decision-making competences. Proponents of both approaches claim capacity to enhance social welfare through better individual decisions. We suggest that such efforts should involve a more careful analysis of how individual and social welfare are related in the policy context. First, (...) individual rationality is not always sufficient or necessary for improving collective outcomes. Second, collective outcomes of complex social interactions among individuals are largely ignored by the focus of both nudge and boost on individual decisions. We suggest that the design of mechanisms and social norms can sometimes lead to better collective outcomes than nudge and boost, and present conditions under which the three approaches (nudge, boost, and design) can be expected to enhance social welfare. (shrink)

Psykologian identiteetti tieteenalana ja ammattikuntana on jännitteinen. Etenkin akateemisissa yhteyksissä psykologia pyrkii usein erottautumaan muista ihmistieteistä metodisesti luonnontieteitä ja niiden kvantitatiivisia menetelmiä lähellä olevana tutkimusalana. Ihmismielen tutkimuksessa neurotieteen rooli on viime vuosikymmeninä kasvanut merkittävästi, ja parhailla julkaisufoorumeilla psykologisten tulosten tukemisesta aivotason evidenssillä on tullut lähes välttämättömyys. Toisaalta kuten Vesa Talvitie kirjassaan Arkipsykologiasta Aivotutkimukseen – kysymyksiä psykologian filosofiasta toistuvasti osoittaa, akateemisen tutkimuksen ulkopuolella työskentelevän psykologin tarkastelukulma ihmismieleen on tyypillisesti toisenlainen. Työuupumuksesta kärsivää tai läheisen kuolemasta masentunutta asiakasta ei ole mielekästä nähdä vain (...) kognitiivisten ja affektiivisten prosessien kokoelmana, vaan tämän tietoisuus ja kokemusmaailma tulee ottaa vakavasti. Asiakas- ja hoitotyössä arkipsykologian mukainen kuva mielestä muodostaa psykologin ja asiakkaan välisen vuorovaikutuksen perustan. Ihminen nähdään mielentilojensa (esimerkiksi uskomusten, halujen, pelkojen tai suunnitelmien) perusteella käyttäytyvänä vapaana ja vastuullisena toimijana. Toisaalta harva kiistää, että mieli jollakin tapaa asuu aivoissa tai väittää ettei aivojen toiminnan ymmärtäminen olisi mielen toiminnan ymmärtämisen kannalta hyödyllistä. Vaikka psykologia tutkii ihmismieltä, löytyy psykologian sisältä löytyy lukuisia näkemyksiä siitä, miten mielen käsite tässä yhteydessä tulisi ymmärtää. Kun psykologit vielä itse ovat usein haluttomia reflektoimaan tieteenalansa käsitteellisiä perustoja, syntyy psykologian filosofian systemaattiselle tutkimukselle selvä tilaus. (shrink)