Millstein, Roberta L.. Probability in Biology: The Case of Fitness
2016,
-
Expand entry
-
Added by: Barbara Cohn, Contributed by: Anya PlutynskiAbstract: I argue that the propensity interpretation of fitness, properly understood, not only solves the explanatory circularity problem and the mismatch problem, but can also withstand the Pandora's box full of problems that have been thrown at it. Fitness is the propensity (i.e., probabilistic ability, based on heritable physical traits) for organisms or types of organisms to survive and reproduce in particular environments and in particular populations for a specified number of generations; if greater than one generation, 'reproduction' includes descendants of descendants. Fitness values can be described in terms of distributions of propensities to produce varying number of offspring and can be modeled for any number of generations using computer simulations, thus providing both predictive power and a means for comparing the fitness of different phenotypes. Fitness is a causal concept, most notably at the population level, where fitness differences are causally responsible for differences in reproductive success. Relative fitness is ultimately what matters for natural selection.Comment: I use this in discussions of natural selection and probability in evolution.
2009, In Leila Haaparanta (ed.), The Development of Modern Logic. Oxford University Press
-
Expand entry
-
Added by: Franci MangravitiAbstract:
The chapter is an overview of Indian logic, with a general introduction followed by specialized sections on four different schools: Nyāya logic, Buddhist logic, Jaina logic, and Navya-Nyāya logic.
Comment: Can be used as a general reference for a course focusing on Indian logic. The various sections are independent, so each can on its own serve as a reading in any course wanting to include discussion of a particular system of logic (e.g. a general logic course, or a course in Indian philosophy).
Morgan, Mary S.. The curious case of the prisoner’s dilemma: model situation? Exemplary narrative?”
2007, Science Without Laws: Model Systems, Cases, Exemplary Narratives. Science and cultural theory, ed. by Creager, Angela N. H., Lunbeck, Elizabeth, Norton Wise, M., Duke University Press, Durham, 157-185
-
Expand entry
-
Added by: Björn Freter, Contributed by: Anna Alexandrova
Abstract: The Prisoner’s Dilemma game is one of the classic games discussed in game theory, the study of strategic decision making in situations of conflict, which stretches between mathematics and the social sciences. Game theory was primarily developed during the late 1940s and into the 1960s at a number of research sites funded by various arms of the U.S. military establishment as part of their Cold War research.
Comment: I assign this piece to give students a sense of where Prisoner's Dilemma comes from and what its ubiquity teaches us about economics (that laws matter less than exemplary situations).
Morris, Rebecca Lea. Intellectual Generosity and the Reward Structure of Mathematics
2021, Synthese, 199(1): 345-367.
-
Expand entry
-
Added by: Fenner Stanley TanswellAbstract:
Prominent mathematician William Thurston was praised by other mathematicians for his intellectual generosity. But what does it mean to say Thurston was intellectually generous? And is being intellectually generous beneficial? To answer these questions I turn to virtue epistemology and, in particular, Roberts and Wood's (2007) analysis of intellectual generosity. By appealing to Thurston's own writings and interviewing mathematicians who knew and worked with him, I argue that Roberts and Wood's analysis nicely captures the sense in which he was intellectually generous. I then argue that intellectual generosity is beneficial because it counteracts negative effects of the reward structure of mathematics that can stymie mathematical progress.Comment (from this Blueprint): In this paper, Morris looks at ascriptions of intellectual generosity in mathematics, focusing on the mathematician William Thurston. She looks at how generosity should be characterised, and argues that it is beneficial in counteract some of the negative effects of the reward structure of mathematics.
Morrison, Margaret. Fictions, representations, and reality
2009, In Mauricio Suárez (ed.), Fictions in Science: Philosophical Essays on Modeling and Idealization. Routledge.
-
Expand entry
-
Added by: Jamie CollinSummary: Uses Maxwell's model of the ether as a case study in accounting for the role of fictions in science. Argues that we should understand idealisation and abstraction as being different from fiction. Fictional models for Morrison are those that are deliberately intended to be such that the relationship between their structure and the structure of the concrete systems they model is not (immediately) apparent. This is different from mere idealisation, where certain structural features are omitted to make calculations more tractable.Comment: Very useful as a primary or secondary reading in an advanced undergraduate course on philosophy of science (or perhaps on philosophy of fiction). It is philosophically sophisticated, but also treats the science in enough detail to provide students with some clear ideas about the nature of scientific representational practices themselves. Would be appropriate in sections on scientific representation or modelling.
Morrison, Margaret. Spin: All is not what it seems
2007, Studies in History and Philosophy of Science Part B 38(3): 529-55.
-
Expand entry
-
Added by: Laura JimenezAbstract: Spin is typically thought to be a fundamental property of the electron and other elementary particles. Although it is defined as an internal angular momentum much of our understanding of it is bound up with the mathematics of group theory. This paper traces the development of the concept of spin paying particular attention to the way that quantum mechanics has influenced its interpretation in both theoretical and experimental contexts. The received view is that electron spin was discovered experimentally by Stern and Gerlach in 1921, 5 years prior to its theoretical formulation by Goudsmit and Uhlenbeck. However, neither Goudsmit nor Uhlenbeck, nor any others involved in the debate about spin cited the Stern-Gerlach experiment as corroborating evidence. In fact, Bohr and Pauli were emphatic that the spin of a single electron could not be measured in classical experiments. In recent years experiments designed to refute the Bohr-Pauli thesis and measure electron spin have been carried out. However, a number of ambiguities surround these results - ambiguities that relate not only to the measurements themselves but to the interpretation of the experiments. After discussing these various issues the author raises some philosophical questions about the ontological and epistemic status of spin.Comment: The goal of the paper is to uncover and isolate how spin presents problems for traditional realism and to illustrate the power that theories like quantum mechanics have for shaping both philosophical questions and answers. It is adequate for higher-level postgraduate courses in Philosophy of Science.
Morrison, Margaret and, Mary S. Morgan. Models as mediating instruments
1999, In M. S. Morgan and M. Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press.
-
Expand entry
-
Added by: Jamie CollinSummary: Morrison and Morgan argue for a view of models as 'mediating instruments' whose role in scientific theorising goes beyond applying theory. Models are partially independent of both theories and the world. This autonomy allows for a unified account of their role as instruments that allow for exploration of both theories and the world.Comment: Useful as a primary or secondary reading in an advanced undergraduate course on philosophy of science, particularly within a section on scientific modeling. The paper is particularly useful in teaching because it is not unduly technical.
Moss, Sarah. Epistemology Formalized
2013, Philosophical Review: 122(1): 1-43.
-
Expand entry
-
Added by: Jie GaoAbstract: This paper argues that just as full beliefs can constitute knowledge, so can properties of your credence distribution. The resulting notion of probabilistic knowledge helps us give a natural account of knowledge ascriptions embedding language of subjective uncertainty, and a simple diagnosis of probabilistic analogs of Gettier cases. Just like propositional knowledge, probabilistic knowledge is factive, safe, and sensitive. And it helps us build knowledge-based norms of action without accepting implausible semantic assumptions or endorsing the claim that knowledge is interest-relative.Comment: Suitable for an upper-level undergraduate courses or master courses on epistemology or formal epistemology. It is good for teachings on topics of the relation between credence and knowledge, and pragmatic encroachment.
Müller-Hill, Eva. Formalizability and Knowledge Ascriptions in Mathematical Practice
2009, Philosophia Scientiæ. Travaux d'histoire et de philosophie des sciences, (13-2): 21-43.
-
Expand entry
-
Added by: Fenner Stanley TanswellAbstract:
We investigate the truth conditions of knowledge ascriptions for the case of mathematical knowledge. The availability of a formalizable mathematical proof appears to be a natural criterion:
(*) X knows that p is true iff X has available a formalizable proof of p.
Yet, formalizability plays no major role in actual mathematical practice. We present results of an empirical study, which suggest that certain readings of (*) are not necessarily employed by mathematicians when ascribing knowledge. Further, we argue that the concept of mathematical knowledge underlying the actual use of “to know” in mathematical practice is compatible with certain philosophical intuitions, but seems to differ from philosophical knowledge conceptions underlying (*).
Comment (from this Blueprint): Müller-Hill is interested in the question of when mathematicians have mathematical knowledge and to what extent it relies on the formalisability of proofs. In this paper, she undertakes an empirical investigation of mathematicians’ views of when mathematicians know a theorem is true. Amazingly, while they say that they believe proofs have an exact definition and that the standards of knowledge are invariant, when presented with various toy scenarios, their judgements seem to suggest systematic context-sensitivity of a number of factors.
Narayanan, Arvind. The Limits of the Quantitative Approach to Discrimination
2022, James Baldwin Lecture Series
-
Expand entry
-
Added by: Tomasz Zyglewicz, Shannon Brick, Michael GreerIntroduction: Let’s set the stage. In 2016, ProPublica released a ground-breaking investigation called Machine Bias. You’ve probably heard of it. They examined a criminal risk prediction tool that’s used across the country. These are tools that claim to predict the likelihood that a defendant will reoffend if released, and they are used to inform bail and parole decisions.Comment (from this Blueprint): This is a written transcript of the James Baldwin lecture, delivered by the computer scientist Arvind Narayanan, at Princeton in 2022. Narayanan's prior research has examined algorithmic bias and standards of fairness with respect to algorithmic decision making. Here, he engages critically with his own discipline, suggesting that there are serious limits to the sorts of quantitative methods that computer scientists recruit to investigate the potential biases in their own tools. Narayanan acknowledges that in voicing this critique, he is echoing claims by feminist researchers from fields beyond computer science. However, his own arguments, centered as they are on the details of the quantitative methods he is at home with, home in on exactly why these prior criticisms hold up in a way that seeks to speak more persuasively to Narayanan's own peers in computer science and other quantitative fields.
Can’t find it?
Contribute the texts you think should be here and we’ll add them soon!