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Firms face a choice between secrecy, which protects existing intellectual property, and openness, which facilitates learning from others. Their decisions determine interaction rates between firms. Krishna Dasaratha discusses the interaction rates in a learning network.

Image courtesy of interviewee. June 28, 2024

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This video discusses the importance of social networks in the spread of innovative ideas and information, particularly in technological settings like Silicon Valley compared to Route 128 in Boston. It highlights research by Anna Lee Sian, which shows that Silicon Valley's success is due to its highly connected network facilitating idea sharing, whereas Route 128's companies tended to be siloed and less collaborative. The author’s research seeks to understand the factors that determine whether social networks are dense and innovative or sparse and secretive.

Utilizing tools from game and network theory, the author models how firms decide on interacting and investing in research and development, with the incentive to balance learning from others while protecting their own ideas. The findings suggest that certain social network structures experience "phase transitions," meaning small changes in behavior can shift a network from being sparse to highly connected, leading to significantly different innovation outcomes.

The research argues for the need for more interaction among firms to promote innovation, suggesting that government interventions, such as public institutions or labs that do not prioritize secrecy, can enhance connectivity and idea flow. The author also reflects on the role of intellectual property rights in enabling or hindering collaboration, noting that while patents can protect ideas, they may also create barriers to idea sharing.

In summary, the author emphasizes that increasing firm interaction may yield substantial benefits for innovation, advocating for policy measures that encourage collaboration rather than merely subsidizing research and development. Future research directions proposed include exploring how diverse groups contribute to innovation through networking.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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An important part of innovation in many settings can be

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ideas and information spreading

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through social networks of various forms.

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And then these ideas

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and this information getting incorporated into new

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technologies or inventions.

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And so this is an idea that there's a lot

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of empirical evidence from in different

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fields in social science.

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And a real starting point

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for my work is qualitative research by Anna Lee Sian,

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where she compares Silicon Valley

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and the Boston area Route 1 28,

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technology industry in the 1980s.

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And what she finds is Silicon Valley looks a lot more like

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what you alluded to, a highly connected cluster with a lot

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of sharing of ideas that leads to,

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all sorts of new breakthroughs.

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Silicon Valley, sorry,

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route 1 28 had successful companies initially,

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but they were much more siloed in

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their individual firms, had much less interaction

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between them in the long run.

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That's an explanation for why the computer industry

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and the Boston area didn't last

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and Silicon Valley became what it is today.

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And so what my research is doing is it's trying

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to think about from a theoretical perspective,

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what might determine when we get these highly connected

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clusters, when do we get sort of dense social networks

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with a lot of interaction and spread of ideas

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and when do we get sparse

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or social networks that don't drive that as much?

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And what I do is I try and use tools from game theory

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and from network theory to understand what the types

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of social networks we might get are going to look like

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and which of these worlds we end up in.

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And so the basic point there is that firms

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and inventors have reasons to be secretive.

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They can also benefit from interacting with each other

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and learning from each other.

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I try and understand how do those incentives drive the types

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of networks we end in my work, as part of the literature

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that's developed in the last 30

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or so years in microeconomic theory, trying to look at

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how do networks matter for all sorts of economic,

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political, social science situations.

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And essentially the first question this literature asked is

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how can we model how networks form

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and especially how can we model how networks form trying

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to think about the incentives

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of the different agents in the network to actually interact

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with form lengths with each other?

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And so the initial approach, which was really the start

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of this network theory literature, was

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to think about settings where people have a lot

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of control over their links with each other.

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And so the basic model there is each person decide

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who do they wanna be connected to?

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So who do I wanna be friends with or working with

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or signing a treaty with?

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And two people or countries

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or firms end up connected to each other. They both say

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They wanna connect with each other.

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And this has been a great model

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for describing a lot of situations.

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Maybe you wanna describe treaties between different nations.

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Maybe you wanna describe formal research collaborations.

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What I'm doing is saying this is assuming a lot about the

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control people have over their connections

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and their knowledge of who's connecting with who.

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And often in a lot of social situations, if we're sort

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of talking to people informally forming friendships,

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there's a lot more uncertainty.

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It's a lot harder to control exactly

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who you end up talking with.

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And so what I'm using is random networks tools

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for probability theory to say what types

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of networks do we end up with?

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If people decide essentially how much to interact

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with each other in formal terms,

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they're deciding the probabilities of connections

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with different types of people,

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but they don't have exact control over

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who exactly they connect with.

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Um, and it turns out in a lot of ways that's a model

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that looks more like other ones we u use in other parts

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of economics where we can use sort of calculus type tools

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to solve for what networks we end up actually getting.

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Basic idea of the model is you have a large number

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of different firms, you can think of these as small startups

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or inventors, and they're all deciding how much

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to invest in research and development

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and almost also how much to interact with each other.

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And that's really the choice that I care most about.

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Um, and so each of these firms traces a trade off

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where they wanna make new technologies

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to make those technologies.

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It's helpful to learn ideas from others,

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but they don't want others to learn from them

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because they can potentially compete.

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And the way the new technologies work in my model is

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essentially there's building blocks which are basic ideas

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which can be combined into technologies.

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And so this is borrowing from a great paper

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by Marty Weitzman, which, called Recombinant Growth,

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which is saying one

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of the ways we should be thinking about innovation in the

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economy is by combining different ideas to form new

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and more complicated, new more complicated technologies.

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And so what I'd then do in this, that's the basic structure

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of the model, what I try

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and ask is what are the networks that we end up with

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in this model end up looking like

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if we're at what's known as equilibrium?

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So if each firm or each inventor is choosing how much

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to interact and how much to invest optimally given

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what other people are.

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And an important part of my results is that it turns out

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that these types

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of network structures exhibit what's sometimes known

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as phase transitions.

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And what this means is

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that there's a point at which you transition from having

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these sparse networks

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to having these highly connected

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clusters with a lot of innovation.

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And it's called a phase transition

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because actually this transition can happen quite sharply.

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So a pretty small change in behavior can have quite big

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impacts for the amount of innovation.

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And so this is by analogy with physics.

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Um, for example, when we transition,

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when water transition from a liquid to a gas

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or a liquid to a solid, this happens sharply.

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You change the temperature a little bit.

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And we go from being in one state to another

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in the same way these networks can have very different

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structures when people change their behavior a little.

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And so the main result ends up being that

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what you can often get is the equilibrium is

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at this phase transition.

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And so the intuition for this is that these different types

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of networks that have different structures above

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and below this critical threshold,

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turn out to lead to quite different incentives.

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So essentially if you have a highly connected cluster,

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this is gonna make firms wanna be more secretive.

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If you don't have one

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that firms on the other hand gonna wanna try

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and interact more to sort of find ideas from different parts

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of the network to combine them

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and make productive new technologies.

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And so I think the, to to wrap up what the,

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what this space transition result means.

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I think the way to think about it's

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that you're almost getting these highly connected clusters

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that can lead to a lot of innovation.

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But in the absence of some sort of sort of intervention

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by government or by other types of institutions,

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the market itself may have too many incentives privately

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to be secretive to quite deliverance.

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The high level takeaway from the result I just told you is

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that it could be that getting a little bit more interaction,

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getting firms to be a bit less secretive

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and more collaborative could have very larger rewards.

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And so I have several results sort of quantifying that,

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saying the benefits can be quite big.

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And then a question that comes up is what types of,

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what types of policies might actually deliver this?

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And so the first thing I ask about is a

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policy that we see a lot.

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What if you just subsidize research and development?

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So pay pay firms to do more research in various ways.

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And what I find is this has some benefits, it does sort

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of improve welfare, but in my model it's not fixing

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the first order problem.

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And so what I find is you really wanna fix these sort

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of incentives to interact

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and get these highly connected clusters,

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getting the right amount of individual firm RD

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so subsidizing that can have very big benefits.

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So then I ask, how can you actually change

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these network formation patterns?

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And one thing I argue can help with this is,

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and this is in line with sex

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and Ann's explanations for the differences between Boston

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and Silicon Valley, is public institutions that aren't

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as concerned as secrecy.

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And so this could be government labs or universities.

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If you have say Stanford University interacting

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with many researchers, then they can potentially lead

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to the emergence of these highly connected clusters.

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And it's not so much because of the research

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that they're doing, but it's

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because they can serve as what I call informational

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intermediaries, learning ideas potentially from one private

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firm and fasting them along to another one that can be sort

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of the central component of one

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of these highly connected clusters.

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There's a huge amount of work

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in economics thinking about incentives towards innovation

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and secrecy

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and competition within sort

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of more traditional property rights frameworks.

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And what I told you so far is trying

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to think less about formal property rights,

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formal collaborations and more about

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what I think is something

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that I think has been less emphasized in a lot of,

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at least the economic theory literature,

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which is more informal types of spread of information

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and collaboration and so on.

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And so, so far what I've told you about, I'm assuming

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that firms don't have any property rights,

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and that ideas are only spreading informally.

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Of course that's not how the real world works.

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There's a mix of more informal spread of ideas

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and formal property rights and collaborations.

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And so I have a section on the paper

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that I would say is just trying to explore some

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of the forces that show up

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when you include both of these in.

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So what my patents do,

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and I think the point there is just

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that patents can be helpful, they can solve some

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of these problems, but they also have outside.

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So one advantage to patents in my model is if firms can

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patent their ideas, then they may not need to worry

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as much about secrecy.

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And so they may be more willing to interact.

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On the other hand, if firms patent their ideas,

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it can make it more difficult for other firms to use them.

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Of course you can have licensing agreements and so on,

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but it'll baseline, it may be that firms interact more,

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but the ideas they're learning are ideas

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that they can't incorporate

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or are concerned about incorporating

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because they may get sued for infringement

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or may have to pay a licensing fee and so on.

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And so I think the basic point here is baseline world is

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one without formal property rights,

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there are ways in which formal property rights may be able

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to help in some cases, but they're likely not going to fix

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these problems completely in many cases.

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Um, and for example, often it could be

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that having some patents can be helpful,

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but you wanna sort of restraint patents

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to particular subset of items.

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I think high level takeaway is motivated by a lot

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of great empirical work on how the spread of ideas

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matter for innovation.

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I'm trying to plug that into a more

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formal theoretical model.

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And what that finds is

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that there can be potentially quite big benefits

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to getting more interaction.

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Um, and that can often be a first order policy

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concern in thinking about how

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to encourage innovation, sometimes

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Potentially more important than things like targeting

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how much research is an individual company doing

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and then tried to point out that

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firms individual incentives to be secretive can mean

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that markets could be very inefficient

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at providing innovation.

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There's a number of ways one could try to fix that.

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One that I've tried to particularly advocate for is,

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building up more public institutions

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that can play an important role in innovation,

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but other types of approaches like property right,

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could work in some settings, future directions.

12:29.775 --> 12:32.895
I think something I've been particularly interested in is

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essentially the model I told you about is one where

12:38.945 --> 12:42.815
firms are treated as apia asym, symmetric.

12:43.875 --> 12:46.195
I have a little bit of some extensions in the paper,

12:46.675 --> 12:48.915
relaxing that a bit, but I think there's a lot of richness

12:50.855 --> 12:54.995
in how social networks connect people from different groups.

12:55.095 --> 12:57.875
So for example, there's a lot of recent research on how

12:58.915 --> 13:02.065
immigrants can play an important role in innovation

13:02.085 --> 13:03.185
and I think a lot of that's

13:03.185 --> 13:05.265
because of sort of spread of ideas, combination of ideas.

13:05.605 --> 13:08.705
So something I've been excited about thinking about is how

13:09.905 --> 13:14.295
about how do different types of, people, inventors,

13:14.655 --> 13:15.935
companies interact with each other?

13:16.195 --> 13:19.655
How might those differences matter for this process

13:19.795 --> 13:22.175
of the spread of in combination of ideas.

"The Dynamics of Innovation: Exploring the Impact of Social Networks on Technological Advancement"

00:00:00 - An important part of innovation in many settings can be ideas and information spreading through social networks of various forms. And then these ideas and this information getting incorporated into new technologies or inventions. And so this is an idea that there's a lot of empirical evidence from in different fields in social science. And a real starting point for my work is qualitative research by Anna Lee Sian, where she compares Silicon Valley and the Boston area Route 1 28, technology industry in the 1980s. And what she finds is Silicon Valley looks a lot more like
00:00:43 - of sharing of ideas that leads to, all sorts of new breakthroughs. Silicon Valley, sorry, route 1 28 had successful companies initially, but they were much more siloed in their individual firms, had much less interaction between them in the long run. That's an explanation for why the computer industry and the Boston area didn't last and Silicon Valley became what it is today. And so what my research is doing is it's trying to think about from a theoretical perspective, what might determine when we get these highly connected clusters, when do we get sort of dense social networks
00:01:24 - and when do we get sparse or social networks that don't drive that as much? And what I do is I try and use tools from game theory and from network theory to understand what the types of social networks we might get are going to look like and which of these worlds we end up in. And so the basic point there is that firms and inventors have reasons to be secretive. They can also benefit from interacting with each other and learning from each other. I try and understand how do those incentives drive the types of networks we end in my work, as part of the literature that's developed in the last 30
00:02:07 - how do networks matter for all sorts of economic, political, social science situations. And essentially the first question this literature asked is how can we model how networks form and especially how can we model how networks form trying to think about the incentives of the different agents in the network to actually interact with form lengths with each other? And so the initial approach, which was really the start of this network theory literature, was to think about settings where people have a lot of control over their links with each other.
00:02:47 - who do they wanna be connected to? So who do I wanna be friends with or working with or signing a treaty with? And two people or countries or firms end up connected to each other. They both say They wanna connect with each other. And this has been a great model for describing a lot of situations. Maybe you wanna describe treaties between different nations. Maybe you wanna describe formal research collaborations. What I'm doing is saying this is assuming a lot about the control people have over their connections and their knowledge of who's connecting with who. And often in a lot of social situations, if we're sort
00:03:27 - there's a lot more uncertainty. It's a lot harder to control exactly who you end up talking with. And so what I'm using is random networks tools for probability theory to say what types of networks do we end up with? If people decide essentially how much to interact with each other in formal terms, they're deciding the probabilities of connections with different types of people, but they don't have exact control over who exactly they connect with. and it turns out in a lot of ways that's a model that looks more like other ones we u use in other parts of economics where we can use sort of calculus type tools

"Phase Transitions in Innovation Networks: Balancing Secrecy and Collaboration Among Firms"

00:04:08 - Basic idea of the model is you have a large number of different firms, you can think of these as small startups or inventors, and they're all deciding how much to invest in research and development and almost also how much to interact with each other. And that's really the choice that I care most about. and so each of these firms traces a trade off where they wanna make new technologies to make those technologies. It's helpful to learn ideas from others, but they don't want others to learn from them because they can potentially compete. And the way the new technologies work in my model is
00:04:50 - which can be combined into technologies. And so this is borrowing from a great paper by Marty Weitzman, which, called Recombinant Growth, which is saying one of the ways we should be thinking about innovation in the economy is by combining different ideas to form new and more complicated, new more complicated technologies. And so what I'd then do in this, that's the basic structure of the model, what I try and ask is what are the networks that we end up with in this model end up looking like if we're at what's known as equilibrium? So if each firm or each inventor is choosing how much
00:05:34 - what other people are. And an important part of my results is that it turns out that these types of network structures exhibit what's sometimes known as phase transitions. And what this means is that there's a point at which you transition from having these sparse networks to having these highly connected clusters with a lot of innovation. And it's called a phase transition because actually this transition can happen quite sharply. So a pretty small change in behavior can have quite big impacts for the amount of innovation. And so this is by analogy with physics. for example, when we transition,
00:06:15 - or a liquid to a solid, this happens sharply. You change the temperature a little bit. And we go from being in one state to another in the same way these networks can have very different structures when people change their behavior a little. And so the main result ends up being that what you can often get is the equilibrium is at this phase transition. And so the intuition for this is that these different types of networks that have different structures above and below this critical threshold, turn out to lead to quite different incentives. So essentially if you have a highly connected cluster, this is gonna make firms wanna be more secretive.
00:06:54 - that firms on the other hand gonna wanna try and interact more to sort of find ideas from different parts of the network to combine them and make productive new technologies. And so I think the, to to wrap up what the, what this space transition result means. I think the way to think about it's that you're almost getting these highly connected clusters that can lead to a lot of innovation. But in the absence of some sort of sort of intervention by government or by other types of institutions, the market itself may have too many incentives privately to be secretive to quite deliverance. The high level takeaway from the result I just told you is
00:07:38 - getting firms to be a bit less secretive and more collaborative could have very larger rewards. And so I have several results sort of quantifying that, saying the benefits can be quite big. And then a question that comes up is what types of, what types of policies might actually deliver this? And so the first thing I ask about is a policy that we see a lot. What if you just subsidize research and development? So pay pay firms to do more research in various ways. And what I find is this has some benefits, it does sort of improve welfare, but in my model it's not fixing the first order problem.

"Facilitating Innovation Through Informal Collaboration and Public Institutions"

00:08:19 - of incentives to interact and get these highly connected clusters, getting the right amount of individual firm RD so subsidizing that can have very big benefits. So then I ask, how can you actually change these network formation patterns? And one thing I argue can help with this is, and this is in line with sex and Ann's explanations for the differences between Boston and Silicon Valley, is public institutions that aren't as concerned as secrecy. And so this could be government labs or universities. If you have say Stanford University interacting
00:08:59 - to the emergence of these highly connected clusters. And it's not so much because of the research that they're doing, but it's because they can serve as what I call informational intermediaries, learning ideas potentially from one private firm and fasting them along to another one that can be sort of the central component of one of these highly connected clusters. There's a huge amount of work in economics thinking about incentives towards innovation and secrecy and competition within sort of more traditional property rights frameworks. And what I told you so far is trying
00:09:38 - formal collaborations and more about what I think is something that I think has been less emphasized in a lot of, at least the economic theory literature, which is more informal types of spread of information and collaboration and so on. And so, so far what I've told you about, I'm assuming that firms don't have any property rights, and that ideas are only spreading informally. Of course that's not how the real world works. There's a mix of more informal spread of ideas and formal property rights and collaborations. And so I have a section on the paper that I would say is just trying to explore some of the forces that show up when you include both of these in.
00:10:19 - and I think the point there is just that patents can be helpful, they can solve some of these problems, but they also have outside. So one advantage to patents in my model is if firms can patent their ideas, then they may not need to worry as much about secrecy. And so they may be more willing to interact. On the other hand, if firms patent their ideas, it can make it more difficult for other firms to use them. Of course you can have licensing agreements and so on, but it'll baseline, it may be that firms interact more, but the ideas they're learning are ideas that they can't incorporate or are concerned about incorporating because they may get sued for infringement
00:10:59 - And so I think the basic point here is baseline world is one without formal property rights, there are ways in which formal property rights may be able to help in some cases, but they're likely not going to fix these problems completely in many cases. and for example, often it could be that having some patents can be helpful, but you wanna sort of restraint patents to particular subset of items. I think high level takeaway is motivated by a lot of great empirical work on how the spread of ideas matter for innovation. I'm trying to plug that into a more
00:11:39 - And what that finds is that there can be potentially quite big benefits to getting more interaction. and that can often be a first order policy concern in thinking about how to encourage innovation, sometimes Potentially more important than things like targeting how much research is an individual company doing and then tried to point out that firms individual incentives to be secretive can mean that markets could be very inefficient at providing innovation. There's a number of ways one could try to fix that. One that I've tried to particularly advocate for is,

"Exploring the Role of Social Networks in Innovation and Idea Exchange"

00:12:21 - that can play an important role in innovation, but other types of approaches like property right, could work in some settings, future directions. I think something I've been particularly interested in is essentially the model I told you about is one where firms are treated as apia asym, symmetric. I have a little bit of some extensions in the paper, relaxing that a bit, but I think there's a lot of richness in how social networks connect people from different groups. So for example, there's a lot of recent research on how
00:13:19 - and I think a lot of that's because of sort of spread of ideas, combination of ideas. So something I've been excited about thinking about is how about how do different types of, people, inventors, companies interact with each other? How might those differences matter for this process of the spread of in combination of ideas.

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