#AttentionEconomy is something of a buzzword among startups in the social media business, but the idea of “managing attention” has a long history as a design philosophy and marketing strategy. The idea has also found some use in the cognitive sciences. The term itself traces to Herbert Simon, a computer scientist and one of the pioneers of Artificial Intelligence. I plan to discuss all these uses of the term "Attention Economy" in future posts, especially Simon's work (which I know best).
But for now, I'll be talking about the Attention Economy as a way of modeling attention behavior in a complex, organized system of attenders. This is technical, and it will take a long and careful analysis to parse what this means in clear and precise ways. We'll need to do some math. However, this approach is in line with work being done across many disciplines, in both the physical and social sciences, in the study of #complexity and #complexsystems If you feel comfortable with the idea (and mathematics) of complexity, you might want to just skip ahead to the good bits and read this article, which was just published in Nature:
I was not involved with this research, but everything I hope to say will be of a piece with the science and methodology presented there. In a future post I will go through this article in detail. However, the paper is difficult and we need to know why we are doing it. In the next post I want to motivate the approach by giving you a simple, intuitive model for thinking about your role in the Attention Economy. Understanding how the model works will be an important tool for understanding the discussion that will follow. In this post, however, I want to lay down the basic picture of how the Attention Economy operates.
I'm talking about the Attention Economy in the present tense, because the article above makes it clear that an Attention Economy is already operational:
... the abundance of information to which we are exposed through online social networks and other socio-technical systems is exceeding our capacity to consume it. Ideas must compete for our scarce individual and collective attention. As a result, the dynamic of information is driven more than ever before by the economy of attention...
You can think of the “economy of attention” as our method for collectively organizing and managing our total “attention resources”, which are finite and must be distributed selectively among all the things you spend your time doing. The paper attempts to model this "dynamic of information" that already exists in the Attention Economy, in this case by measuring Twitter users and their management of screen space for their tweets. Measuring the attention economy within that ecosystem is only the tiniest fraction of the overall Attention Economy, but this ecosystem provides a model for thinking about the general case. Visualizations of the model’s dynamics are snapshots of the growth of an organized social system and are absolutely fascinating:
These models of attention are generated by describing the "diffusion of memes" in terms of the probability that a person will retweet a meme. You can see the description of this “minimalist model of information spreading” here:
As I said, we’ll go over the details of this article in a later post. The point for now is that we can generate models for characterizing the way this crowd of Twitter users manage their attention, and these models are predictive of their collective behavior. These models can’t predict an individual’s chances of retweeting some particular tweet better than giving the probabilities, but we can nevertheless describe the dynamics of the whole system, and those dynamics tell us a lot about how the system is behaving.
This is part of what it means to describe the collective behavior of the Twitter users as complex. Complex doesn’t mean “unpredictable” or “magical”, it means that the system in question can be viewed from many different perspectives, some of which might seem incommensurable with one another, but each of which can be adopted to build genuinely useful predictive models. Consider the global climate system, a paradigm case of a complex system. Specifically, consider the relationship between local weather events and climate events. While it is (so far) very difficult to model weather at a sufficiently detailed scale to predict (say) the fall of individual raindrops, we have pretty good models for predicting--even very far into the future!--what the prevailing conditions will be at a particular place and time. It’s worth emphasizing that both these perspectives--the perspective of the system as a collection of local, short-time-scale weather events and the perspective of the system as a collection of global, long-lived, very large climate conditions--are both different ways of looking at the same system. Moreover, the two perspectives are (at least to a degree) independent of one another: we can adopt one or the other to make accurate predictions about the system without necessarily paying much attention to what’s going on at the other level(s). Complex systems are pattern rich systems: systems that can be modeled from many different perspectives. There is more (much more) to say about complexity. If you are interested, we’ll be dealing with it a lot, so you might start here:
And if you are ambitious: http://necsi.edu/publications/dcs/
The purpose in this series of posts is to describe how this modeling of attention would work in contexts beyond the behavior of Twitter users sharing tweets. The meme diffusion model described in Weng et. al. has obvious and significant applications beyond these cases; even still, the simple Twitter model already yields some surprising conclusions, which move the authors to the following dramatic call to action:
Our results do not constitute a proof that exogenous features, like intrinsic values of memes, play no role in determining their popularity. However we have shown that at the statistical level it is not necessary to invoke external explanations for the observed global dynamics of memes. This appears as an arresting conclusion that makes information epidemics quite different from the basic modeling and conceptual framework of biological epidemics. While the intrinsic features of viruses and their adaptation to hosts are extremely relevant in determining the winning strains, in the information world the limited time and attention of human behavior are sufficient to generate a complex information landscape and define a wide range of different meme spreading patterns. This calls for a major revision of many concepts commonly used in the modeling and characterization of meme diffusion and opens the path to different frameworks for the analysis of competition among ideas andstrategies for the optimization/suppression of their spread.
My goal, our goal, is to take up this call.
An Attention Economy in the grandest sense is the situation where all system management decisions are made by appeal to models describing the flow and concentration of attention across a network of connected attenders. Although this is an idealized case framed in technical jargon, my view is that using attention models will increasingly be preferred to other kinds of economic models (especially financial models) as the primary tools for social organization. Right now, financial models are responsible for nearly all social organizing decisions: both for the distribution of labor and resources but also for the policy decisions that shape our systems of governance. I will argue later on that attention models are fundamentally a measure of consensus and therefore may function as the legitimate grounds for a self-organized system of governance, while at the same time working as a model for collectively planning the production, distribution, consumption, and recycling of our natural resources. In this sense, an Attention Economy is a complete system for social organization, and therefore may function in the ideal case without either money or centralized political institutions significantly determining the results. Although we are far from the ideal case, this systemic reorganization is simplifying and unifying, and is a fundamental component in the package of solutions that humanity has been slowly preparing as the systemic failures of the late 20th century have highlighted its undeniable weaknesses. I think the Attention Economy is the kind of fundamental reorganization that will prepare us for the century we currently find ourselves in. I also think that we are already well into the development of an Attention Economy. Still, this is not an easy solution and our success is by no means certain. It will require cooperation on a global scale to reorganize our social institutions equitably and peaceably, and the sooner we understand how it works the sooner we can start to prepare. I am getting far ahead of myself, but I hope we discuss all these things with far more depth and care.
The most immediate and obviously pressing issue is: what does this look like forme, as an individual, as a member of a society organized by attention? Am I justa node in the network?
I cannot answer this question with any precision; such outcomes very much depend on the steps we take from here. I have important things to say about the humanitarian and digital values that inform the directions I would hope to see a mature attention-based economy grow. I believe that if realized properly, an attention economy will generate profoundly liberating and creative human actions, at a scale suited to the incredible challenges we face. But this is all lofty idealism, and there will be time for that later. I plan to continue these posts on a weekly basis, and in the next post I want to give you a simple, intuitive description of how an attention economy would work on an individual, practical, day-to-day scale. I will describe a simple idea, a toy model, that is meant to make the behavior of an attention economy understandable and intuitive, yet give some sense for how those actions scale up to the level of complex systems management on the order of global human populations. As we continue to discuss the Attention Economy, this model will be a useful reference point for describing the dynamics of our future. Part of the fun will be figuring out the formal structure of the toy model as we go.
Original Post: https://plus.google.com/u/0/117828903900236363024/posts/Rsk9wDvSP5i