New post out on my Substack!
We're often faced with uncertainty, but what should we do if we can choose how to learn about the world? I discuss a really cool paper that answers this question:
blueprintsofemergence.substack.com/p/the-burden...
All comments/suggestions are welcome! I’m happy to hear what people think about this.
Here is a link to the paper: arxiv.org/abs/2510.10997
This result extends to heterogeneous agents, where types might correspond to location, sectors, etc. The same force that captures these amplifying effects is still there, which suggests that it might be lurking even in more complex models.
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The intuition here is that when you are forming a connection, in addition to getting the instantaneous value from it, you increase the probability that structures are formed in the future. When these spillovers are reinforced enough, we can get phase transitions.
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Perhaps the most fascinating implication of this result is that networks can discontinuously respond to changes in motif values. This is a phenomenon called “phase transitions”. This means that even a small change in parameters can populate or destroy the network!
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I show that the motif model converges to a (directed) Erdös-Rényi model, where the density solves the optimization problem below.
It intuitively says that agents try to form structures that maximize their motif values, but it is costly to explore the vast space of networks.
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We can specify a finite set of motifs, and this fully determines the incentives of players to form the network. This is useful to study how the long-run behavior of the process depends on the incentives to form structures.
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It could also be the case that if someone reciprocates a link, both individuals receive some benefit.
These are examples of motifs: recurring structures whose value doesn’t depend on who participates, only on the structure of connections. The examples above look like this:
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Usually the values we assign to structures are highly regular. If we encounter the same structure, just with different people, we might give it the same value.
For example, forming a link might have a fixed cost, regardless of who forms the link or who they connect to.
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There is still a complication in our analysis of the network formation process. Even if we find the potential, the space of networks is huge. For example, there are more networks with 20 than atoms in the universe! If we want interpretable results, we need to do better.
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When the game is a potential game, the stationary distribution has the following form, called a Gibbs measure. What is Φ? It’s the potential of the game!
This means that the long-run properties of the process have a clear relation to the static properties of the game.
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Let’s go back to your social network. Over time you (and everyone else) will face the choice of forming or severing friendships many times. This gives rise to a Markov chain of networks, whose stationary distribution tells us about the long-run behavior of the process.
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What happens when everyone values a structure equally? Then the game is a potential game. This means that there exists a single function (the potential) that captures the incentives to deviate of all players. Focusing on potential games greatly simplifies our analysis.
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I show that these two approaches are equivalent, and how to switch between the two. Thinking about sub-structures turns out to be particularly useful, since they capture the incentives to change the network. This makes analyzing things like equilibria much cleaner.
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Adding this friendship will change how much you value the social network. You can think about your choice in two ways:
1. How would this friendship change your valuation of the whole network?
2. Which new sub-structures does this link create, and how much do you value them?
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Networks are fluid objects. Regardless of the application you’re interested in, chances are links are being created and destroyed all the time.
Let’s think about your social network. You might bump into someone new and think about whether you want to be their friend.
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New working paper!
Have you ever wondered how the way you form friendships, partnerships, etc affects the structure of how society as a whole interacts?
It turns out that even small changes to individual incentives can have huge aggregate effects!
A 🧵:
Our understanding of neural computation -- both in the brain and artificial networks -- is founded on an assumption: That neurons fire in response to a linear sum of inputs
We systematically test this assumption 👇 arxiv.org/abs/2504.08637
Modern supply chains don't look like trade theory 101!
They involve constant border crossings, each now hit by tariffs.
Tariffs raise prices, but the more important thing they do is disrupt supply relationships.
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i'm excited to be judging a Quora knowledge contest
questions are below.
if you like thinking about economics, write good answers to these questions!!
first prize is $1000
second prize is $200 (which can buy a set of steak knives)
contests.quora.com/Now-Open-Jan...
a hot (nerd) take
sometimes in economics the more mathy people care about writing their proofs pedantically with all details carefully spelled out
interestingly many, if not most, professional mathematicians do not write their proofs this way
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How can physics define universal laws for computation, from cells and chips to brains? In this @pnas.org Perspective, led by @sfiscience.bsky.social David Wolpert and Jan Korbel, they suggest how Stochastic Thermodynamics can provide the proper framework pnas.org/doi/epub/10....
delighted to see this prize go to Arun Chandrasekhar, a pioneer in applications and theory of networks in economics and social science more broadly
a fantastically talented and energetic scholar
This is great! I’d like to join too, will share new work soon
I've made a starter pack for the Economics of Networks! 🕸️ This includes economic and social networks and wide range of methods: applied/econometrics and theory. I'm sure I have missed a lot. Please DM/reply with others, including self-nominations.
go.bsky.app/4yrQJsw
I had a lot of fun talking to Liza about my journey through economics and physics! Check out the episode (and her podcast) here:
open.spotify.com/episode/0myM...
