It's not uncommon to hear people say that computer simulations "just aren't philosophy."

No more!

Conor Mayo-Wilson and I have a new paper where we argue that computer simulation should be seen as a *core* philosophical method.

philsci-archive.pitt.edu/18100/
Our argument proceeds via a comparison with thought experiments. We first develop a list of purposes for engaging in thought experiments in philosophy. We then argue that computer simulations achieve many of these -- sometimes better than thought experiments.
On this later point, we appeal to the problem of complex social systems to motivate why thought experiments may be particularly poorly suited for this topic. But, many philosophers have used thought experiments about social systems anyway (eg Kant, Hobbes, Locke, Hume).
We also argue that the process of building a simulation (even one that is never actually used) is helpful in uncovering hidden assumptions and being precise in one's thinking. These are virtues that philosophers prize, and so are reasons to include simulations as methods.
We then consider several common objections to using computer simulations in philosophy. These objections pertain to issues like: correctness, validation, robustness, and the like.
To forestall misunderstanding, we are not trying to replace current philosophical methods. Nor are we arguing that *every* philosopher should be a simulator. But, we argue that philosophers should embrace simulations as a core method.
For those who know simulations in the sciences, much of what we say will be totally familiar. Our hope was to adapt these arguments explicitly for a philosophical audience who might think either philosophy is unique or might not have encountered simulations before.

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More from @KevinZollman

18 Aug
I think we, as teachers, need to be on the lookout for how our own judgment about the right way to teach might be effected by common cognitive and inductive biases. A thread...🧵
Bias #1: survivorship bias. This is the bias where you look at a population that has been selected for one reason and assume that features of that selected group represents the population at large.

en.wikipedia.org/wiki/Survivors…
One example: Conservativism. Everyone who is now a professor likely enjoyed the way a topic was taught (otherwise, they wouldn't have become a professor). So they are resistant to reforms because they remember liking it being taught one way.
Read 15 tweets
11 Jun
This is why one has to be careful when thinking about scientific "consensus": When @nytimes surveyed 511 epidemiologists about the future, they got a small number of very bizarre answers from scientists.
~20 scientists said that they expected to never again hug or shake hands with a friend.

~7 said they never expected to go out with someone they didn't know well again.

~5 expected to continue wearing masks forever.

~5 would never again get a haircut at a salon.
There are probably explanations for all of this. Perhaps those people already don't shake hands, go out with someone they don't know well, or cut their hair.
Read 7 tweets
30 Apr
This was the last week for my network epistemology class (sad emoji). We focused on models of citation networks, the network formed by how papers cite other papers.

This is an enormous literature, so we focused on one question: why might citations fail to track quality?
This question is incredibly important because increasingly scientists, journals, and papers are evaluated by using various citation metrics. Hiring, firing, promotion, tenure, subscription, and search all depend on these metrics. If they don't track quality... things are bad.
We started with this paper by @mollymking, @CT_Bergstrom, Correll, @jenniferjacquet, and @jevinwest.

Through the analysis of a giant data set, they show that men cite themselves significantly more than do women. (And the gap isn't decreasing)

journals.sagepub.com/doi/full/10.11…
Read 25 tweets
22 Apr
Today in my Network Epistemology class we discussed the well known phenomena "Pluralistic Ignorance." The idea of pluralistic ignorance is that people may privately believe one thing, but publicly profess another. (Privately they may hate the regime, but publicly praise it.)
The Anderson story "The Emperors New Clothes" is often used as a motivating example. Each person privately believes the Emperor is naked but is afraid to say it because they worry they may be revealing themselves as uncultured.

en.wikipedia.org/wiki/The_Emper…
We started by reading this paper, which seeks to clarify the concept and asks the question: is pluralistic ignorance always the result of individual irrationality? Or could rational groups come to be pluralistically ignorant?

link.springer.com/article/10.100…
Read 19 tweets
16 Apr
This week in my Network Epistemology class we looked at models of argumentation in social networks. "Argumentation" here is not to be taken negatively -- like when you say someone is argumentative. Instead, it's meant as the method of conversation where you exchange ideas.
Most models of information spread and belief aggregation look at how one belief is affected by social influence. For example, models of probabilistic pooling all assume we are "pooling" a single belief with others.

But in reality, of course, we sometimes interact by arguing with one another. I tell you what I believe and I also give you *evidence* for that belief. That evidence might be something that you already believe, and so I might convince you through argumentation.
Read 19 tweets
9 Apr
This week in my Network Epistemology class, we looked at the analogy between the spread of ideas and the spread of disease. (I planned to do this topic before the pandemic, but it seems especially relevant now.)
The underlying idea is that many process involve diffusion of ... something ... through a population. That "something" might be a disease, it might be an idea, it might be a mutation, or it might be a technology. Perhaps the underlying models might be the same.
In epidemiology, the basic model is the SIR model (S = susceptible, I = infectious, R = resistant or recovered). This model is called a "binning" or "compartmental" model because we assume that the population is compartmentalized into three different groups S, I, and R.
Read 16 tweets

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