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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  • It certainly seems generally true that people are less motivated by informational exchange than they purport to be but aren’t you biasing the sample by considering only “news”. I mean isn’t time spent studying an academic discipline, getting various IT certifications and most other professional development activity essentially exactly what you suggest people should be doing: learning information about the world that wasn’t just discovered?

    Isn’t news in some sense just the name for the market for information people want merely because it is current? Or are you suggesting people aren’t showing enough interest in history specifically as opposed to news?

    • David Condon

      There are far fewer people reading those alternative sources than the the number of people reading the news. Most people have picked up a newspaper or magazine once or twice. Far fewer read textbooks after they’ve already graduated college.

  • You switch between “chuck” and “chunk” a few times. Neither sounds especially nice, but the former lends itself more to how-the-sausage-gets-made analogies.

    • Robin Hanson

      Oops; thanks, fixed.

  • Sharper

    An experience many people can relate to at some point of another is reading a news story where they happen to know a lot about the subject of story, either because of profession or proximity or whatever. Usually, they figure out the stories on that specific topic aren’t very good. Ideally, they’d then generalize that experience to suggest most news stories, even the ones on topics they don’t know enough about to compare, are just as inaccurate.

    In terms of missing information, it’s gotten so bad that sometimes stories headline something, then completely fail to actually include any details. Yesterday I happened to see a headline in Google news about how the House was going to have to re-vote on the Tax Bill because Democrats in the Senate had brought up that three provisions in the proposed bill violated Senate rules and forced them to be dropped. Curious about which 3 provisions the Senate parliamentarian had ruled out, I clicked on the first news article to read it.

    It wasn’t until the 7th article I read on the topic that one of them (NY Times) thought to include any information on the actual provisions being blocked (one of which turned out to be the title of the bill). You really have to go to great lengths to write 10-15 paragraphs in an article on the subject of “3 provisions in Tax Bill blocked by Democrats” without managing to mention what the provisions actually were. Is the reporting just that poor, or is it bias, in the sense of proclaiming the information they thought people would be excited by (Democratic victory on something!) and hiding the information which might tend to discredit the narrative (not much victory!).

    Either way, somehow a good framework would need to distinguish the motivation for providing the information, as that seems to override the portions you’d expect to see based on an honest attempt to provide straight information. This isn’t just a recent problem, either. It’s been a standard issue since the early days of the printing press.

  • xyz

    Do you discuss Herman and Chomsky’s ‘propaganda model’?

    • Robin Hanson

      Not in the book, no.

  • Russ Andersson

    Robin … this is an interesting perspective and your central point is very valid There is a lot else missing/wrong from a purely correct theory perspective here but I guess the major point you are trying to make is that news sources provide low information value.

    For example:

    “Okay, the amount of info that some news gives a reader on a claim is the ratio of its confidence to its surprise”

    This is not correct, the information value its the difference between these two … and information value should not be confused with economic value. (how much its worth in dollars and sense to have this information) And if you are interested I can model this out for you very simply.

    In fact there is a large body of work extending basic Shannon information theory to more human questions very much like you have asked here, what is the “value” of information, but most of it is in this 1966 seminal paper by Ronald Howard:

    • Robin Hanson

      The ratio is the likelihood ratio, which is what you can combine via multiplication across multiple news items to give total news. You can’t do that with the difference.

      I’m well aware of the value of info literature.

      • Russ Andersson

        Perhaps I misunderstood you but when I try apply your definitions/methodology to a real world scenario it doesn’t seem to add up to me. Take something that many people care about … like Trump and the probability he will end his term as president.

        Please can you help me reconcile the following scenario per your own definitions and methodology?

        Claim: Probability of trump ending his term as president.

        Surprise – how low a probability a reader would have previously assigned to this claim. For me its 75%.

        Then I hear credible “news” that he has been assassinated …

        Confidence – how high a probability a reader is to assign after reading this news. This is now 0%. No chance.

        Based on your calculation the “amount” of information from that news … the ratio of confidence/surprise which is 0%/75% … = 0 as in ZERO

        Then you go and multiply 0 with importance and then multiply that by commonality etc and you still get 0.

        Which basically means credible news of Trump being assassinated, has a value of … 0?

        What am I missing Professor?

      • Robin Hanson

        When giving formulas, I had in mind applying them where the news was that a claim had *risen* in probability. Once can of course represent them with the complement event whose probability has lowered, but that is a lot less natural.

      • Russ Andersson

        This definitional change doesn’t solve the fundamental issue that its is the absolute difference and not the ratio that needs to be used as the central element before multiplication … of this I am highly confident ( :)) and now that I have bought this to your attention, I am sure you know that you can model this out with dice and cards etc to prove it for yourself.

        But the big picture here is you deserve considerable credit for putting together a really interesting question and model so lets not get too hung up on mathematical minutiae, the big point I wanted to make was the value of info literature had addressed a fair amount of this and you are aware of that so we are all good.

        With that minor caveat out of the way, I think you are asking some very probing questions here … why do we have limited regard for the value of information anywhere in society? Why are we a society that doesn’t really invest in information at all … basically why are we so foolish?

        my high level response is that we get what we deserve and that as humans our lack of real respect and acceptance of the truth/reality causes us to harm ourselves in very material ways. News being merely one of them. If we were more sincere about finding and acting on the truth/reality then we would probably be more happy and well adjusted beings.

  • Silent Cal

    The genre one might call “Gladwellian nonfiction” runs on the premise of “shared explanation and justification of many related small claims”–such books cover multiple apparently unrelated claims, united by using the same concepts as support.

    (Though I suppose the *explicit* justification is that the various topics all provide support for a central claim)

    • Robin Hanson

      Yes, there’s a disconnect between the official explicit rationale and the actual one.

  • David Condon

    With respect to recency, it’s easier to get everybody on the same page by focusing heavily on the latest. If a news organization doesn’t focus on the latest but instead picks a particular topic, but nobody else picks that same topic, that’s going to make it a lot harder to bring up that same topic around non-readers around the water cooler, or to get noticed in search engines.

    • Robin Hanson

      Sure, but why from an info point of view would it be important to get everyone on the same page?