Who are you?
I'm a guest researcher at the Max Planck Institute for Evolutionary Anthropology in Leipzig, where I live. Previously I was a postdoctoral researcher at the Surrey Morphology Group. I received my PhD in philosophy of biology from the Australian National University in December 2020.
I am applying for postdoctoral positions in philosophy of science in central and western Europe. If you hear of any, let me know!
I answer questions like
- in what sense do biological signals carry information? and
- how are naturally evolved systems like designed systems? and
- what is the role of selection in cultural evolution?
I won't publish, referee, or do editorial work for Elsevier journals and I am in good company.
What have you written?
(forthcoming) Free energy: a user's guide [with Ross Pain and Michael Kirchhoff]
(forthcoming) Teleosemantics and the free energy principle [with Ross Pain]
2022 Teleosemantics and the hard problem of content [with Ross Pain]
2020 CHIELD: The Causal Hypotheses in Evolutionary Linguistics Database [with Seán Roberts and 25 others]
2020 Consequences of a functional account of information
2018 Attribution of information in animal interaction
2018 [Review of] Studying Animal Languages without Translation by Zhanna Reznikova [with Jessica Pfeifer]
What are you writing now?
Relational explanation in causal models
Are you guest editing any Topical Collections in the journal Biology & Philosophy?Funny you should ask. With Ross Pain and Michael Kirchhoff I am guest editing the Topical Collection The Free Energy Principle: From Biology to Cognition. The point is to illuminate the relevance of Karl Friston's free energy principle for explanations in biology and cognitive science. There will eventually be 10-12 articles and you can read them as they are published.
What was your PhD about?
The manuscript is available at the Australian National University repository.
How can the concepts and results of communication theory aid evolutionary biology? This thesis argues for an explanatory framework, evolutionary communication theory, that interprets and illuminates scientific research into the phenomenon of biological signalling. By expanding the theory beyond the models and goals familiar to Claude Shannon and other engineers, real insight is gained into how strategic interplay between senders and receivers shapes signal form. Furthermore, interpreting artificial and natural signals in terms of sender-receiver teleosemantics demonstrates the explanatory role of relations borne between signals and world affairs. One of the major results of the thesis is a rejection of the orthodox distinction between Shannon and semantic information. While there are at least two useful distinctions to be drawn -- between cues and signals, and between statistical and functional content -- the terminological confusion that gave rise to the phrase `Shannon information' should be put aside for good.
- Chapter 1 outlines a way to capture the relationships between signals and other signal-like interactions using a multi-dimensional conceptual space called a hypercube. I argue that sender-receiver teleosemantics is uniquely well suited to capturing those aspects of communication theory that render it a viable mathematical framework for evolutionary biology.
- Chapter 2 discusses an early attempt to apply communication theory in evolutionary biology. Haldane & Spurway's informational interpretation of the honeybee waggle dance has recently been criticised on mathematical grounds. These criticisms lend support to scepticism about the relevance of information for evolutionary biology. I argue that the criticisms are themselves mathematically erroneous, so one route to scepticism about information is undercut. [See also my paper Attribution of information in animal interaction.]
- Chapter 3 explores a related line of scepticism. It is common in the philosophy of biology to treat the concepts and tools of communication theory as insufficient or irrelevant for analysing semantic content. I argue that the grounds of this supposition are based on misinterpretations of some features of communication theory.
- In chapter 4 I reconstruct Millikan's teleosemantics in a causal-modelling setting, highlighting the explanatory role of semantic content.
- In chapter 5 I respond to objections to the teleosemantic account, including the claim that the theory renders explanations of success that appeal to semantic content circular. I also argue for an interpretation of important features of communication-theoretic models in terms of teleosemantics.
- Chapter 6 explores another challenge to applying teleosemantics to biological signals. The theory places emphasis on cooperation between senders and receivers, but biological signals are often fraught with evolutionary conflict. I discuss recent formal work, and argue that prospects for teleosemantics are good.
- Finally, in chapter 7 I argue that an explanatory framework that draws on communication-theoretic concepts would be beneficial to evolutionary biology. I present case studies of communicative behaviour for which biologists offer explanations that are well interpreted through the principles of communications engineering.
Any conferences and workshops?
Nov 2019 The mechanics of representation: teleosemantics meets the free energy principle [with Ross Pain]
May 2019 We should get rid of the distinction between Shannon information and semantic information
Dec 2018 Prediction and Uncertainty [with Ross Pain]
Sept 2018 Applied cultural evolution: Studying language change with evolutionary methods
June 2018 Communication and selection
Feb 2018 Unitrackers in artificial and natural cognition
How about a CV?Here you go!
Have you done any philosophy outreach?
In 2022 I wrote a popular science/philosophy of biology essay called Major Transitions: A Record of Reorganization of Individuality in the Colonial Organisms of Earth for Life Beyond Us, an anthology soon to be published by the European Astrobiology Institute.
In 2019 at ANU I filmed a bunch of interviews with a bunch of smart people on the subject of ethics in artificial intelligence. A few of them are collected in this YouTube playlist.