11 September 2018 ~ 0 Comments

The Struggle for Existence in the World Market Ecosystem

The global trade market definitely seems red in tooth and claw. Competition is fierce and some claim it has to be that way: only the fittest should survive because the fittest, at least in theory, are the ones satisfying the customers’ needs the best. In a paper with Viviana Viña-Cervantes and Renaud Lambiotte, we explored this metaphor to see if we can say something about how world trade will evolve. The paper will soon appear in PLoS One and it is titled “The Struggle for Existence in the World Market Ecosystem“. In it we create competition networks and we show that positions in these networks are a predictor of future growth: a strong position in a product means that the country will increase its export share in that product in the medium term.

How do we create competition networks? In our view, each slice of the market is an ecological niche. For instance, the car market in the United States or the computer market in Germany. If you can sell a lot of cars in the US, it means you’re fit to occupy that niche. You’re meeting the needs of that set of customers, and thus you can make a profit there, cutting out other exporters who would oh so much like to make a buck there.

An example of data generating one edge in the competition network: Japan emerges beyond 1% of the car market in the US at the same time that Italy plunges below the 1% mark. With this data, we create an edge from Japan to Italy in the US-car 1960 competition network.


Niches are not stable: they change over time. As a consequence of evolution, animals can become fitter to fill their current niche — or a new one. Out of this observation, we create our competition networks. Suppose that you were doing well at selling cars to Americans, like Italy was in the 60s — who doesn’t love a vintage Alfa Romeo?  Then something happens: a mutation, an evolution. Japan’s cars start gaining appeal in North America. All of a sudden, the market share of Italy declines once Japan appears on the scene. In this case, we can create a directed edge going from Japan to Italy, because Japanese firms happened to be successful at the same time that Italian ones lost their, um, edge.* That’s our competition network. We built one per decade: 1960, 1970, 1980, 1990, and 2000.

In the vast majority of cases, when you study a network, the edges have a positive meaning: they imply connections, social relations, friendship. Competition networks tell a fundamentally negative story. The originator of the edge is displacing the receiver of the edge in a market, something definitely not nice. The out-degree tells us something about a country’s fitness: how many competitors it displaced. The in-degree is the other side of the coin: how many times the country’s entrepreneurs were just not good enough. So these two measures should tell us what we want, right? A high out-degree is a sign of strength and growth, a high in-degree a sign of weakness.

The correlation between in- and out-degree is pretty darn high.

Not really. The problem is that big countries produce a lot of stuff and export it everywhere. So they are constantly fighting a lot of battles. Winning some and losing some. The in- and out-degree are highly correlated, and thus they do not give much information. We decided to look at a generalization of in- and out-degree. When displacing a country from a market, quantity is not as important as quality. Displacing a fierce exporter like the US is not the same as tripping up a weaker economy. So we weight the out-degree by counting each displaced country as much as their out-degree. This is a higher-order degree, because it looks at a second hop beyond the displaced. The more this country was a displacer, the more it counts that we were able to displace it. Displacing the displacers is the way to go.

At this point interesting stuff starts emerging. We can use this normalized in- and out-degree to classify countries into three buckets: out-competing (high out-degree), displaced (high in-degree), and transitioning (roughly equivalent in- and out-degree). We do so per decade and per product. Then, we check whether belonging to one cluster has any relationship with how the country will evolve its market share in the decade following the one we used for the classification. If you were a strong out-competitor in the 60s in the car market, your position in the car market in the 70s will get stronger and stronger.

The growth rate the decade after the observation window used for classifying countries. Here: 1 = Displaced, 2 = Transitioning, and 3 = Out-competing countries.

We see these strong relationships for all products and for all decades, with a couple of exceptions. For instance, our method does not work for natural resources. Which is expected: we cannot use this system to predict whether you’re going to find oil in your territory or not. It also does not work in the last decade, the 2000s, because we have very little data for making the prediction: our data runs only until 2013. Thus, it means this method cannot work for short term predictions: it works well when looking at decade-long transitions, not year-long ones. The effect gets a bit weaker if we look at what happens two, three and even four decades after our classification, but it’s still significant.

We also checked the robustness of our results by creating a synthetic trade world. We broke all relationships between countries by generating random trade, maintaining the sparseness — most exporter-importer-product relationships never happen — and the skewed nature of our data — a few high-throughput links constitute most of world trade, and the vast majority of links are low-value ones. In this world with random competition, we see far fewer links in our networks. Using the ratio between in- and out-degree also doesn’t work: as predictor, it returns a much lower result quality.

The average growth rate for out-competing countries when the prediction period is one, two, three or four decades away from the observation one.

So, to wrap up, in the paper we show how to build competition networks, by connecting strong emerging economies to the ones they are out-competing for specific products. Analyzing the higher-order relationships in this networks — i.e. going beyond the simple degree — uncovered a way to estimate the real strength of these emerging countries. A lot of questions remain unanswered. Chief among them: what if we ensured that each edge in the competition networks is truly causal? This will be a quest for another time.

* We’re extremely aware of the fiendish operation we did: these competition networks are absolutely correlational and will never imply causation. However we believe there’s also value in looking at these serendipitous events. If nothing else, at least some econometrician might have had a stroke reading the original sentence.

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20 May 2013 ~ 3 Comments

Memetics, or: How I can spend my entire day on Reddit claiming that I’m working

In his 1976 book “The Selfish Gene“, Richard Dawkins proposed a shift in the way we look at evolution: instead of considering the organisms as the center of evolution, Dawkins proposed (providing tons of evidence) to consider single genes as the fundamental evolution unit. I am not a biologist nor interested in genetics, so this idea should not concern me. However, Dawkins added one chapter to his book. He felt that it could be possible that culture, too, is made out of self-replicating units, just like genes, that can compete and/or collaborate with each other in forming “cultural organisms”. He decided to call these units “memes”.

The idea of memes was mostly in the realm of intellectual and serious researchers (not like me); you can check out some pretty serious books like “Metamagical Themas” by Hofstadter or “Thought Contagion: How Belief Spreads Through Society” by Lynch. But then something terrible was brought to the world. Then, the World Wide Web happened, bringing with itself a nexus of inside jokes, large communities, mind hives, social media, 2.0s, God knows what. Oh and cats. Have one, please:

With the WWW, studying memes became easier, because on the Internet every piece of information has to be stored somehow somewhere. This is not something I discovered by myself, there are plenty of smart guys out there doing marvelous research. I’ll give just three examples out of possibly tens or hundreds:

  • Studies about memes competing for the attention of people in a social network like “Clash of the contagions: Cooperation and competition in information diffusion” or “Competition among memes in a world with limited attention” ;
  • Studies about the adoption of conventions and behaviors by people, like “The emergence of conventions in online social networks”or “Cooperative behavior cascades in human social networks”;
  • Studies about how information diffuses in networks, like “Virality and susceptibility in information diffusions” or “Mining the temporal dimension of the information propagation” which, absolutely incidentally, is a paper of mine.

There is one thing that I find to be mostly missing in the current state of the research on memes. Many, if not all, of the above mentioned works are focused in understanding how memes spread from one person to another and they ask what the dynamics are, given that human minds are connected through a social network. In other words, what we have been studying is mostly the network of connections, regardless of what kinds of messages are passing through it. Now, most of the time these “messages” are about penguins that don’t know how to talk to girls:

and in that case I give you that you can fairly ignore it. But my reasoning is that if we want to really understand memes and memetics, we can’t put all of our effort in just analyzing the networks they live in. It is like trying to understand genes and animals and analyzing only the environment they inhabit. If you want to know how to behave in front of a “tiger” without ever having met one, it is possibly useful to understand something about the forest it is dwelling in, but I strongly advise you to also take a look at its claws, teeth and how fast it can run or climb.

That is exactly what I study in a paper that I got accepted at the ICWSM conference, titled “Competition and Success in the Meme Pool: a Case Study on Quickmeme.com” (click to download). What I did was fairly simple: I downloaded a bunch of memes from Quickmeme.com and I studied the patterns of their appearances and upvotes across a year worth of data. Using some boring data analysis techniques borrowed from ecology, I was able to understand which memes compete (or collaborate) with which other ones, what are the characteristics of memes that make them more likely to survive and whether there are hints as the existence of “meme organisms” (there are. One of my favorites is the small nerd-humor cluster:


One of the nicest products of my paper was a simple visualization to help us understand the effect of some of the characteristics of memes that are associated with successful memes. As characteristics I took the number of memes in competition and in collaboration with the meme, whether the meme is part of a coherent group of memes (an “organism”) and if the meme had a very large popularity peak or not. The result, in the picture below (click to enlarge), tells us an interesting story. In the picture, the odds of success are connected by arrows that represent the filters I used to group the memes, based on their characteristics.

This picture is saying: in general, memes have a 35.47% probability of being successful (given the definition of “successful” I gave in the paper). If a meme has a popularity peak that is larger than the average, then its probability of success decreases. This means that, my dear meme*, if you want to survive you have to keep a low profile. And, if you really can’t keep a low profile, then don’t make too many enemies (or your odds will go down to 6.25%). On the other hand, if you kept a low profile, then make as many enemies as you can, but only if you can count on many friends too, especially if you can be in a tightly connected meme organism (80.3%!). This is an exciting result that seems to suggest that memes are indeed collaborating together in complex cultural organisms because that’s how they can survive.

What I did was just scratching the surface of meme-centered studies, as opposed to the network-centered meme studies. I am planning to study more deeply the causal effect between a meme and its fitness to survive in the World Wild Web and to understand the mechanics of how memes evolve and mutate. Oh, and if you feel like, I am also releasing the data that I collected for my study. It is in the “Quickmeme” entry under the Datasets tab (link for the lazies).

* I deeply apologize to Dawkins, any readers (luckily they are few) and to the scientific community as a whole, for my personification of memes. I know that memes have not a mind, therefore they can’t “decide” to do anything, but it really makes it so much easier to write!

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