 I'm going to be talking about funding positive some goods. I'm Divya, I'm a political economist at Microsoft Research, the Radical Exchange Foundation, in the process of starting the Collective Intelligence Project. And this is really a concept I've been working out. I think it's a concept a lot of people here have been working out and thinking about. So this is sort of me inviting you into my process, so to speak. So it's not totally fleshed out yet, I would say. And I think as an intro, we're all here to enable positive some features. To align individual incentives with the common good. And this talk is really about how we can think about what constitutes that collective good and how we can better achieve it through hybrid goods funding and hybrid governance mechanisms. And I'm going to start out with something that we've all seen maybe a million times, which is this traditional two by two of how we think about economic goods. And these come with sort of implicit modes of provisioning, right? We imagine the state will fund public goods and communities will govern commons and the markets will sort of basically take care of the rest I think is the typical imaginary. But real life, as we know, does not separate super cleanly into these buckets. So we can imagine perhaps adding some granularity. We can imagine adding semi rival goods, which are only partially depleted by other people's using them, something like a museum where maybe it's a public good but really becomes depleted quite quickly if it gets crowded. And that's why these goods are often called congestible. We can add something like semi-excludable goods. So they're only partially excludable but not directly provided for cost. For example, something that is a PDF that has DRM on it so that it's difficult to share and to pirate. And we can even imagine possibly extending this into something that's more of a continuum. We can go from sort of rivalry to non-rivalry as a continuous sphere and same with excludability. And maybe that gives us a little bit more granularity. We get a little bit closer to the truth in terms of really determining where different goods fall. But I think the difficulty with thinking kind of in this flat space at all is sort of recognizing that these goods are not fixed into categories, right? I mean, I think that's a big part of the problem with these kinds of mechanisms, which is that where these goods end up is a matter of choice, not of pre-existing characteristics. And so it reminds us that goods aren't forever in one category or another. I mean, famously, the reason we have private property as all is because a lot of land was moved from that commons bucket into the private bucket, right? And in a lot of different ways, we can still do that for various types of goods that we might associate with one bucket or another. So the PDF we were talking about earlier, I mean, for that, excludability is a cost that we can choose to pay or not to pay. It's not that something is excludable by design, it's the fact that we are choosing to pay the money to make it excludable or not. So something like a children's playground, for example, we could easily make it excludable. You can build a wall around it and you can have someone outside and they can be collecting money for every kid that comes in, right? And we often don't choose to do that because socially, that's not the kind of world we want to create. And so there are certain things that we choose to pay the price for excludability on and certain things that we don't. And I think a general principle that kind of emerges from looking at this for a while is we often want to be closed or exclude as little as possible to maintain maximum increasing returns. So we recognize that there's some amount of excludability that enables innovation, that enables more provision and things like that. We want to do as little as possible of that for the maximum amount of return to the network. And so I think that's kind of the balance we're often striking and it's important to remember therefore that we can move goods around into this sort of more broad space of rivalry and excludability to sort of enable that outcome. And so we've seen that excludability is about cost, not about attributes. So are you willing to pay to make something excludable? Rivalry may kind of feel like less of a choice, right? Maybe you buy me on the excludability argument but you're not totally convinced that rivalry is also something you can move around which makes sense. I mean, I can't just decide that something that's consumable like a cup of coffee is suddenly going to become non-rival. That's something that if I consume it you can't consume it. That's just a fact about that good. And that is true in some cases but I will argue that it's often not true. And I think that's because this two by two misses a crucial element which is anti-rival goods. And Evan and I have talked about this and I think he mentioned it earlier in the talk that I unfortunately missed. But anti-rival goods are goods that one adds positive value by using the good that can be experienced by others. Term was coined by Professor Stephen Weber at Berkeley to describe open source software but it can be extended to categories far beyond it. Discoveries and inventions like solar cells, that's an anti-rival good. The more people use it, the more that ecosystem grows and it's positive for everyone. Ideas are in this category. Systems of law and institutions are in this category. I mean, liberal democracy for example, one adds positive value if you participate in that system to others in that system. Protocols, blockchain protocols, standards like TCPIP. There are so many types of goods that underpin our systems that are really in many cases anti-rivalry. Sorry, anti-rival risk. And so what do we do with that, right? And I think there are a couple of specific things that we can keep in mind about anti-rivalry that help us sort of structure the types of goods that we might want to fund in this space. So one is that anti-rivalry tends to be systemic. So take something like language which is sort of a broadly anti-rival risk concept. Individual instantiations of this can be rival or not. A book can be very rival, a printed book, right? And it is a way that people get brought into this anti-rival risk network of language, but the overall system is better the more that people use it. If I'm speaking a language that no one else speaks, that's useless for me, right? I can own, the more people speak English as an English speaker, the better it is for me to speak that language, but very much certain parts of English are very much rival risk. So we have to think about this balance. And I think this is where we think about, it can apply differently to different aspects of the same goods. So think of something like a traffic light. The physical object is clearly rival, can't be replicated at zero marginal cost. Usage is non-rival, so unlike some of the book categories, if I look at a traffic light and stop, you hopefully are also doing that at zero cost to me. But at the systemic level, it's anti-rival. Following the rules of a traffic light, following broad traffic systems that we all mostly agree with, at least, it's much better for you if other people do it too. It's very difficult if people don't, right? So in fact, we realize that there are probably optimal trade-offs between rivalry and non-rivalry in particular instantiations that can create this anti-rival system. So that's one piece. And then the third point is with anti-rivalry, people don't just benefit from sharing. It's often the case that they benefit in a rough proportion to the amount of sharing. So this can change the way we manage goods. Instead of aiming for excludability, which we talked about earlier, sometimes you need to aim for excludability to enable innovation and things like this, we actually want to enable not only permissiveness, but to sometimes actively inclusion, because the shared benefit to the network is proportional to the amount of inclusion that you have. So these are different ways that anti-rivalry, if we don't think about goods in this way, we may forget that these are all kind of functions of anti-rivalrous goods that are different from the traditional categories. And I would say the conclusion from what we've gone through so far from the two by two, recognizing goods can be moved around and thinking about this concept of anti-rivalry is goods can be made anti-rival and more important systems can be made anti-rival. And I would argue this is a core part of what we should be aiming for at a systemic level and trying to fund the commons or fund public goods and all of these kinds of things that we talk about. And on top of that, making a system anti-rival means quite strategically figuring out how to apportion exclusion within that system. So an anti-rival ecosystem definitionally requires funding some rival goods, some excludable goods, with things like communication networks and scientific research and institutions, you need to fund some of those things to make the network possible. And if we do the right balance, we may get something like positive sum networks built off of these anti-rival goods. And yet, getting that balance wrong, which I would argue we often do, means sacrificing huge amounts of benefit from people. And it's on that side that I think a lot of our systems are airing. So we can learn from things like the cooperative ecosystem, as Sarah talked about earlier, that have created these positive sum networks and are quite careful about not over-extracting such that they kind of don't create this balance of exclusion and rivalry and anti-rivalry that we might want to see. And I think to make this a little bit more concrete, I'll walk through some examples that I work on, but I think this is the kind of concept that people can definitely apply right to their own ecosystem. So for me, I work a lot on data governance as an input to AI and different types of governance, as an input to tech governance. And this is an ecosystem where I think we can all agree we're not really hitting the optimal trade-off between exclusion and rivalry and anti-rivalry, right? There are some inherent anti-rivalrous qualities to what data is for. There are some inherent concerns to sharing too much. There are privacy concerns, there are extraction concerns. And there are a lot of types of positive sum externalities that you can imagine from sharing data. There's a huge amount of input into scientific research. There's a lot of input into solving public problems. We saw this with COVID, we saw this with climate change. There's a lot you can get from more sharing. And yet we've set up a system where there are certain entities that end up hovering up huge amounts of this data to the detriment of others, right? So we've taken a good that could be non-excludable and could be non-rival. We've made it excludable, which sometimes is good for privacy reasons. We've made it excludable only by a few people and we've created an entire marketplace around preserving that excludability. So a bunch of what I work on is, and this is a picture from when I made less pretty slides for another presentation, but creating this kind of network ecosystem of data cooperatives and coalitions to mediate basically some of these problems, to make sure that we have more of a hand in when we're excluding people from certain types of data, when we're creating networks and when we're regulating that system as a whole, whether that is by platforms or by government, all of that comes into play in trying to create an anti-rival system. And another example that I spend a fair bit of time on is on internet protocols. So similarly, we created an ecosystem that's incredibly positive sum. Existing internet protocols, HTTP, TCPIP, et cetera, they are some of the most directly positive sum networks that we currently have used to transfer information in this incredible global way. And then there are varying levels of the stack that have gotten captured, right? We talked about identity earlier, just talked about data, payments, communication. These are things that weren't a part of that positive sum network to begin with and now have been captured by entities that provide them at high exclusion. And so we can imagine if instead we were able to build new protocols and standards that allowed for these types of things to be provided in a more anti-rival risk way, then the positive sum externalities we could enable would far outpace what is currently possible, even though what is currently possible is still positive sum. And so a lot of this is about balance. It's about figuring out who should fund these ecosystems and where they should be built. And in the case of the internet, right, there's a great prior because it is, was initially a product of public funding and private funding and academic research and civil society. And as that balance has moved away from being as polycentric as we might want it to be, we see more capture. And so I think this is almost a warning to both say, yes, things that are not purely public goods can be positive sum. So we don't wanna focus on things that are purely public goods. However, if we get the balance wrong, if we do too much excludability and too much rivalry, if we build too much private capture into the system, we're no longer enabling all the externalities that we can. Here are just a bunch of other examples. Time is going faster than I anticipated, so I'll run through them really quickly. Local journalism, I think is a great one, right? You don't want this to be purely publicly funded. Clearly the market isn't providing what we needed to provide. How do we hit that balance? Data I already talked about. Social media platforms have anti-rival risk characteristics, but they could be enabling more positive sum externalities than they currently are, possibly through more open protocols. Scientific research, I know lots of folks are covering, so I won't get into it. Transportation networks, I think are like this. They're sort of the communication networks that we have physically, right? But I think the concluding point is, we can have these anti-rival positive sum networks, but we need to be hitting this right balance. And the last piece I'll say is one of the ways that we can do this is by implementing collective intelligence mechanisms. And this to me is really about aligning who has voice over a decision versus who faces the consequences. And a lot of the spaces in which we see this balance going wrong is when these two things aren't aligned. When it's not the case that the people who are making decisions over the system face consequences over that system. And so we can build these kinds of collective intelligence mechanisms that allow us to align these things better and to make sure that there's a check on what people who are designing a system can imagine is the right balance to strike, to make sure that there's a check to say, are the people who are being affected by this also think that the right balance is being struck? Do institutions think that that's the right balance? Do private companies think that that's the right balance? Do stakeholders think that that's the right balance? Do citizens of a nation state think that that's the right balance? It's the only way we can figure out whether we're doing a good job in cutting off those feedback mechanisms at the root, which I think we're doing in a lot of these networks currently really prevents that from being possible. And so I think these are all sort of mechanisms, technologies and systems we've talked about and we'll talk about in this ecosystem a lot, but these are ways that we can create hybrid collective intelligence mechanisms that enable funding positive some goods. If we're gonna break the binary between public and private, which I think we should, I think we should think about these things as networks instead of certain goods that are public and good and certain things that are private or not, then we need these checks and for public goods that check often comes in the form of the state. The democratically accountable state ideally does this for us, right? Maybe it's not perfect, but it should do it. We no longer have those checks if it's just the people in this room kind of thinking about what would be positive externalities and what wouldn't be the appropriate rate of capture and what wouldn't be. So I would urge us to sort of consistently go back to stakeholders and get a sense of if we're striking that right balance. And that's that. This is my exhortation to you. Thank you. I think we may have two minutes and 48 seconds for questions. Cool. I mean, I had a question. So do you think, are there any heuristics while trying to build towards anti-riverly? Sorry, I'm not mispronouncing it, but you know what I mean. Yeah, it's funny. I kept getting my autocorrect to like anti-viralty. Yeah. Which I think is, yeah, Polish pronunciation, sorry. Appropriate in the age of the pandemic, which we need to capture with anti-rival risk codes. But anyway, yeah, I think there are definitely ways to measure network effects, right? There are ways to measure externalities that we've already talked about. If we think about something like carbon markets, for example, I mean, the idea there is you try to measure a type of externality that we haven't previously measured. I think that type of accounting can be taken in other spaces. One thing that I've advocated before is labor market externality capture. Can we figure out if a technology is having positive effects on the labor market or not? And is that something that you can price in and create these collective intelligence mechanisms around? So I think in that way, we have some of the underlying mechanisms that can do that, but we need to be using them to capture different types of externalities than we currently do, because a lot of what we want to capture is not priced in. Cool, one more question. Thank you, that was really great. So yeah, we talk a lot about DAOs, we talk a lot about crypto-economic systems, and I really love the point that you made about kind of blurring the line or introducing a continuum from public versus private. And we've seen this in traditional business entity structures from for-profit, non-profit, now in the middle you have public benefit, right? And there's only what, like 3,000 public benefit corporations right now, I think, last time I checked. So what I would really be interested in are, do you have any kind of thoughts or maybe suggestions or directions as to how we could from the ground up from a governance using Web Zero technology to embed maybe certain governance mechanisms or business entity structures to be anti-rivalers from the beginning, right? Yeah, definitely. I mean, I think there's a lot of interesting work happening here. I work with the Exit to Community Network that kind of tries to do this, right? How do we have some community ownership built into companies from the beginning so that they can start including these collective intelligence mechanisms? Gitco and I know is speaking next, I don't see Scott here, but I think that their model is a great example of what this could possibly look like. And I think this is where mechanisms come into play because if you can set up, and mechanisms don't just have to be technology, I mean legal trust, purpose trust, there's an organization called Purpose that works on this. If you can set in different types of outcomes that you're optimizing for into a charter early, right? Then that is the same as having some sort of technological mechanism that holds you to capturing externalities, for example. So I think both on the tech side and on the legal side, there are lots of different kind of adjustments in corporate governance structure that can get to this point. And some of that means public pressure as well. Like some of that means now we care about whether a company is carbon neutral or not, that's the kind of thing that we could expand a lot into the different types of positive, some things that we can imagine companies could do. Cool, awesome. I think that's kind of the, pretty much. Is there one more question? One more quick question? If not, that's the time we'll have. Thank you so much. Yeah, this was awesome. Thank you.