Tag Archives: News

News As If Info Mattered

In our new book, we argue that most talk, including mass media news and academic talk, isn’t really about info, at least the obvious base-level info. But to study talk, it helps to think about what it would in fact look like if it were mostly about info. And as with effective altruism, such an exercise can also be useful for those who see themselves as having unusually sincere preferences, i.e., who actually care about info. So in this post let’s consider what info based talk would actually look like.

From an info perspective, a piece of “news” is a package that includes a claim that can be true or false, a sufficient explanation of what this claim means, and some support, perhaps implicit, to convince the reader of this claim. Here are a few relevant aspects of each such claim:

Surprise – how low a probability a reader would have previously assigned to this claim.
Confidence – how high a probability a reader is to assign after reading this news.
Importance – how much the probability of this claim matters to the reader.
Commonality – how many potential readers this consider this topic important.
Recency – how recently this news became available.
Support Type – what kind of support is offered for a reader to believe this claim.
Support Space – how many words it takes to show the support to a reader.
Definition Space – how many words it takes to explain what this claim means.
Bandwidth – number of channels of communication used at once to tell reader about this news.
Chunk – size of a hard-to-divide model containing news, such as a tweets or a book.

Okay, the amount of info that some news gives a reader on a claim is the ratio of its confidence to its surprise. The value of this info multiplies this info amount by the claim’s importance to that reader. The total value of this news to all readers (roughly) multiplies this individual value by its commonality. Valuable news tells many people to put high confidence in claims that they previously thought rather unlikely, on topics they consider important.

A reader who knew most everything that is currently known would focus mostly on recent news. Real people, however, who know very little of what is known, would in contrast focus mostly on much less recent news. Waiting to process recent news allows time for many small pieces of news to be integrated into large chunks that share common elements of definition and support, and that make better use of higher bandwidth.

In a world mainly interested in getting news for its info, most news would be produced by specialists in particular news topics. And there’d be far more news on topics of common interest to many readers, relative to niche topics of interest only to smaller sets of readers.

The cost of reading news to a reader is any financial cost, plus a time cost for reading (or watching etc.). This time cost is mostly set by the space required for that news, divided by the effective bandwidth used. Total space is roughly definition space plus support space. If the claim offered is a small variation on many similar previous claims already seen by a reader, little space may be required for its definition. In contrast, claims strange to a reader may take a lot more space to explain.

When the support offered for a claim is popularity or authority, such support may be seen as weak, but it can often be given quite concisely. However, when the support offered is an explicit argument, that can seem strong, but it can also take a lot more space. Some claims are self-evident to readers upon being merely stated, or after a single example. If prediction markets were common, market odds could offer concise yet strong support for many claims. The smallest news items will usually not come with arguments.

Given the big advantages of modularity, in news as in anything else, we need a big gain to justify the modularity costs of clumping news together into hard-to-divide units, like articles and books. There are two obvious gain cases here: 1) many related claims, and 2) one focus claim requiring much explanation or support. The first case has a high correlation in reader interest across a set of claims, at least for a certain set of readers. Here a sufficient degree of shared explanation or support across these claims could justify a package that explains and supports them all together.

The second case is where a single focal claim requires either a great deal of explanation to even make clear what is being claimed, or it requires extensive detailed arguments to persuade readers. Or both. Of course there can be mixes of these two cases. For example, if in making the effort to support one main claim, one has already done most of the work needed to support a related but less important claim, one might include that related claim in the same chunk.

For most readers, most of the claims that are important enough to be the focus of a large chunk are also relatively easy to understand. As a result, most of the space in most large focused chunks is devoted to support. And as argument is the main support that requires a lot of space, most of the space in big chunks focused on a central claim is devoted to supporting arguments. Also, to justify the cost of a large chunk with a large value for the reader, most large focused chunks focus on claims to which readers initially assign a low probability.

So how does all this compare to our actual world of talk today? There are a lot of parallels, but also some big deviations. Our real world has a lot of local artisan production on topics of narrow interest. That is, people just chat with each other about random stuff. Even for news produced by efficient specialists, an awful lot of it seems to be on topics of relatively low importance to readers. Readers seem to care more about commonality than about importance. And there’s a huge puzzling focus on the most recently available news.

Books are some of our largest common chunks of news today, and each one usually purports to offer recent news on arguments supporting a central claim that is relatively easy to understand. It seems puzzling that so few big chunks are explicitly justified via shared explanation and justification of many related small claims, or that so man big chunks seem neither to cover many related claims nor a single central claim. It also seems puzzling that most focal claims of books are not very surprising to most readers. Readers do not seem to be proportionally more interested in the books on with more surprising focal claims. And given how much space is devoted to arguments for focal claims, it is somewhat surprising that books often neglect to even mention other kinds of support, such as popularity or authority.

While I do think alternative theories, in which news is not mainly about info, can explain many of these puzzles, a discussion of that will have to wait for another post.

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News of What?

Today’s New York Times has a 7000 word article by Amy Harmon on cryonics, brain scanning, and brain emulation. Now these are subjects of great interest to me; my first book comes out in spring on the third topic. And 7000 words is space to say a great deal, even if you add the constraint that what you say must be understandable to the typical NYT reader.

So I’m struck by the fact that I have almost nothing to say in response to anything particular said in this article. Ms. Harmon gives the most space to one particular young cryonics patient who got others to donate to pay for her freezing. This patient hopes to return via brain emulation. Ms. Harmon discusses some history of the Brain Preservation Prize, highlighting Ken Hayworth personally, and quotes a few experts saying we are nowhere close to being able to emulate brains. At one point she says,

The questions the couple faced may ultimately confront more of us with implications that could be preposterously profound.

Yet she discusses no such implications. She discusses no arguments on if emulation would be feasible or desirable or what implications it might have. I’ll give her the benefit of the doubt and presume that her priorities accurately reflect the priorities of New York Times readers. But those priorities are so different from mine as to highlight the question: what exactly do news readers want?

For a topic like this, it seems readers want colorful characters described in detail, and quotes from experts with related prestige. They don’t want to hear about arguments for or against the claims made, or to discuss further implications of those claims. It seems they will enjoy talking to others about the colorful characters described, and perhaps enjoy taking a position on the claims those characters make. But these aren’t the sort of topics where anyone expects to care about the quality or care of the arguments one might. It is enough to just have opinions.

Added 14Sep: Amy posted a related article that is a technical review of brain emulation tech. I’m glad it exists, but I also have nothing particular to say in response.

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Why News?

Google Alerts has failed me. For years I’d been trusting it to tell me about new news that cites me, and for the last few years it has just not been doing that. So when I happened to go searching for news that mentions me, I found 135 new articles, listed on my press page. I’d probably find more, if I spent a few more hours searching.

Consider for the moment what would have happened if I had put up a blog post about each of those press articles, as they appeared. Even if I didn’t say much beyond a link and a short quote, some of you would have followed that link. And the sum total of those follows across all 135 articles would be far more than the number of you who today are going to go browsing my press page now that you know it has 135 new entries.

Similarly, I now have 2829 scholarly citations of my work, most of which appeared while I was doing this blog, and this blog has had 3640 posts, many of which were written by others when this was a group blog. So I might plausibly have doubled the number of my posts on this blog by putting up a post on each paper that cited one of my papers. Or more reasonably, I might have made one post a month listing such articles.

For both news and academic articles that cite me, I expect readers to pay vastly more attention to them if I announce them soon after they appear than if I give a single link to a set of them a few years later. Yet I don’t think, and I don’t think readers think, that the fundamental interest or importance of these articles declines remotely as fast as reader interest. This is also suggested by the fact that readers follow so many news sources, like blogs, instead of looking at only the ‘best of’ sections of far more sources.

Bottom line, readers show a strong interest in reading and discussing articles soon after they appear, an interest not explained by an increased fundamental importance of recent articles. Instead a plausible hypothesis is that readers care greatly about reading and talking about the same articles that others will read and talk about, at near the time when those others will do that reading and talking. In substantial part, we like news in order to support talking about the news, and not so much because news communicates important information or insights.

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Trends Rarely Inform Policy

I’d like to try to make a point here that I’ve made before, but hopefully make it more clearly this time. My point is: trend tracking and policy analysis have little relevance for each other.

You can discuss education policy, or you can discuss education trends. You can discuss medical policy or you can discuss medical trends. You can discuss immigration policy, or you can discuss immigration trends. And you can discuss redistribution and inequality trends, or you can discuss redistribution and inequality policy. But in all of these cases, and many more, the trend and policy topics have little relevance for each other.

On trends, we collect a lot of data, usually on parameters that are relatively close to what we can easily measure, and also close to summary outcomes that we care about, like income, mortality, or employment. Many are interested in explaining past trends, and in forecasting future trends. Such trend tracking supports the familiar human need for news to discuss and fret about. And when a trend looks worrisome, that naturally leads people to want to discuss what oh what we might do about it.

On policy, we have lots of thoughtful theoretical analysis of policies, which try to judge which policies are better. And we have lots of relevant data analysis, that tries to distinguish relevant theories. Such analysis usually ends up identifying a few key parameters on which policy decisions should depend. But those tend to be abstract parameters, close to theoretical fundamentals. They usually have only a distant relation to the parameters which are tracked so eagerly as trends.

To repeat for emphasis: the easy to measure parameters where trends are most eagerly tracked are rarely close to the key theoretical parameters that determine which policies are best. They are in fact usually so far away that it is hard to judge the sign of the relation between them. This makes it unlikely that a change in one of these policies is a reasonable response to noticing some tracked-parameter trend.

For example which policies are best in medicine depends on key theoretical parameters like risk-aversion, asymmetric info on risks, meddling preferences, market power of hospitals, customer irrationality, and where learning happens, etc. But the trends we usually track are things like mortality, rates of new drug introduction, and amounts, fractions, and variance of spending. These later parameters are just not very relevant for inferring the former. People may find it fascinating to track trends in doctor salaries, cancer deaths, or how many are signed up for Obamacare. But those are pretty irrelevant to which policies are best.

As another example, debates on immigration refer to many relevant theoretical parameters, including meddling preferences, demand elasticity for low wage workers, and the intelligence, cultural norms, and cultural plasticity of immigrants. In contrast, trend trackers talk about trends in immigration, low-skill wages, wage inequality, labor share of income, voter participation, etc. Which might be fascinating topics, but they are just not very relevant for whether immigration is a good or bad idea. So it just doesn’t make sense to suggest changing immigration policy in response to noticing particular trends in these tracked parameters.

Alas, most people are a lot more interested in tracking trends than in analyzing policies. So well meaning people with smart things to say about policy often try to make their points seem more newsworthy by suggesting those policies as answers to the problems posed by troublesome trends. But, in doing so they usually mislead their audiences, and often themselves. Trends just aren’t very relevant for policy. If you want to talk policy, talk policy, and skip the trends.

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