Category Archives: philosophy

people are not points in space

This is the video of a lecture that I gave at the Institute H21 symposium in Prague last September. The symposium was entitled Democracy in the 21st Century: Challenges for an Open Society, and my talk was: “People Are Not Points in Space: Opinions and Discussions as Networks of Ideas.” I’m grateful for the opportunity to present and for the ideas of other participants and organizers.

My main point was that academic research currently disparages the reasoning potential of ordinary people, and this skepticism discourages efforts to protect and enhance democratic institutions. I think the low estimate of people’s capacity is a bias that is reinforced by prevalent statistical methods, and I endorse an alternative methodology.

See also:  individuals in cultures: the concept of an idiodictuon; Analyzing Political Opinions and Discussions as Networks of Ideas; a method for analyzing organizations

can AI help governments and corporations identify political opponents?

In “Large Language Model Soft Ideologization via AI-Self-Consciousness,” Xiaotian Zhou, Qian Wang, Xiaofeng Wang, Haixu Tang, and Xiaozhong Liu use ChatGPT to identify the signature of “three distinct and influential ideologies: “’Trumplism’ (entwined with US politics), ‘BLM (Black Lives Matter)’ (a prominent social movement), and ‘China-US harmonious co-existence is of great significance’ (propaganda from the Chinese Communist Party).” They unpack each of these ideologies as a connected network of thousands of specific topics, each one having a positive or negative valence. For instance, someone who endorses the Chinese government’s line may mention US-China relationships and the Nixon-Mao summit as a pair of linked positive ideas.

The authors raise the concern that this method would be a cheap way to predict the ideological leanings of millions of individuals, whether or not they choose to express their core ideas. A government or company that wanted to keep an eye on potential opponents wouldn’t have to search social media for explicit references to their issues of concern. It could infer an oppositional stance from the pattern of topics that the individuals choose to mention.

I saw this article because the authors cite my piece, “Mapping ideologies as networks of ideas,” Journal of Political Ideologies (2022): 1-28. (Google Scholar notified me of the reference.) Along with many others, I am developing methods for analyzing people’s political views as belief-networks.

I have a benign motivation: I take seriously how people explicitly articulate and connect their own ideas and seek to reveal the highly heterogeneous ways that we reason. I am critical of methods that reduce people’s views to widely shared, unconscious psychological factors.

However, I can see that a similar method could be exploited to identify individuals as targets for surveillance and discrimination. Whereas I am interested in the whole of an individual’s stated belief-network, a powerful government or company might use the same data to infer whether a person would endorse an idea that it finds threatening, such as support for unions or affinity for a foreign country. If the individual chose to keep that particular idea private, the company or government could still infer it and take punitive action.

I’m pretty confident that my technical acumen is so limited that I will never contribute to effective monitoring. If I have anything to contribute, it’s in the domain of political theory. But this is something–yet another thing–to worry about.

See also: Mapping Ideologies as Networks of Ideas (talk); Mapping Ideologies as Networks of Ideas (paper); what if people’s political opinions are very heterogeneous?; how intuitions relate to reasons: a social approach; the difference between human and artificial intelligence: relationships

Hypothetical network of a small group

a method for analyzing organizations

I’m about to conduct a study in partnership with a civic association in the midwestern United States. It should yield insights that can inform this association’s plans and help me to develop a method and related theory. I have IRB approval to proceed, using instruments that are designed.

In the meantime, a colleague alerted me to an impressive new paper by Dalege, Galesic and Olsson (2023) that uses a very similar model. Fig. 1 in their paper resembles the image I’ve created with this post. These authors make an analogy to physics that allows them to write about spin, energy and temperature. I don’t have the necessary background to replicate their analysis but will contribute relevant empirical data from a real-world group and some additional interpretive concepts.

We will ask members of the association to what extent they agree with a list of relevant beliefs (derived from their own suggestions in an open-ended survey). We will ask them whether each belief that the individual endorses is a reason for their other beliefs. As a hypothetical example, you might think that the organization’s youth programming is important because you believe in investing in young people. That reflects a link between your two beliefs. We will also ask members to name their fellow members who most influence them.

In the hypothetical image with this post, the circles represent people: members of the group. A link between any two members indicates that one or both have identified the other as an influence. That is a social network graph.

The small shapes (stars, circles, etc.) represent the beliefs that individuals most strongly endorse. The arrows between pairs of beliefs indicate that one belief is a reason for another. This is a belief-network.

Reciprocal links are possible in both the social network and the belief networks.

Before analyzing the network data, I will also be able to derive some statistics that are not directly observed. For example, each node in both the social network and the belief networks has a certain amount of centrality, which can be measured in various standard ways. I can also run factor analysis on the responses about beliefs to see whether they reflect larger “constructs.” (Again, as a hypothetical example, it might turn out that several specific responses are consistent with an underlying concern for youth, and that construct could be measured for each member.)

I plan to test several hypotheses about this organization. These hypotheses are not meant to be generalizations. On the contrary, I expect that for any given organization, most of the hypotheses will turn out to be false. The purpose of testing them is to provide a description of the specific group that is useful for diagnosis and planning. Over time, it may also be possible to see which of these phenomena are most common under various circumstances.

Hypotheses to test

H1: The group is unified

H1a: The group is socially unified to the extent that its members belong to one network connected by interpersonal influences. The denser the ties within the connected network, the more the group displays social unity.

H1b: The group is epistemically epistemically unified to the extent that members endorse the same beliefs, and to the extent that these shared beliefs are central in their belief networks.

H2: The group is polarized.

H2a: The group is socially polarized if many members belong to two separate subgroups that are connected by interpersonal influences but are not connected to each other, as depicted by the red and blue clusters in my hypothetical image.

H2b: The group is epistemically polarized if many members endorse belief A, and many other members endorse B, but very few or no members endorse both A and B. If A and/or B also have high network centrality for the people who endorse them, that makes the epistemic polarization more serious. (Instead of examining specific beliefs, I could also look at constructs derived from factor analysis.)

H3: The group is fragmented

H3a: The group is socially fragmented if many members are connected by influence-links to zero or just one other member.

H3b: The group is epistemically fragmented if no specific beliefs are widely shared by the members.

H4: The group is homophilous if individuals who are connected by influence-ties are more likely to endorse the same beliefs, or have the same central beliefs, or reflect the same constructs, compared to those who are not connected. If the opposite is true–if socially connected people disagree more than the whole group does–then the group is heterophilous.

H5: There is a core and a periphery

H5a: There is a social core if some members are linked in a relatively large social network, while most other members are socially fragmented.

H5b: There is an epistemic core if many (but not all) members endorse a given belief, or a given belief is central for them, or they share the same constructs, while the rest of the organization does not endorse that belief.

H6: Certain members are bridges

H6a: A person is a social bridge if the whole group would be socially polarized without that person.

H6b: A person is an epistemic bridge if the whole group would be epistemically polarized without that person.

H7: Members tend to hold organized views: This is true if the mean density of individuals’ belief networks (the mean number of links/nodes) is high, indicating that people see a lot of logical connections among the things they believe.

Our survey respondents will answer demographic questions, so we will be able to tell whether polarized subgroups or core groups have similar demographic characteristics. Hypothetically, for example, a group could polarize epistemically or socially along gender lines. And we will ask general evaluative questions, such as whether an individual feels valued in the association, which will allow us to see whether phenomena like social- connectedness or agreement with others are related to satisfaction.

What to do with these results?

Although the practical implications of these results would depend on the organization’s goals and mission, I would generally expect polarization, fragmentation, the existence of cores, and homophily to be problematic. These variables may also intersect, so that an organizations that is socially polarized, epistemically polarized, homophilous, and reflects highly organized views is especially at risk of conflict. A group that is fragmented and reflects disorganized belief-networks at the individual level may face a different kind of risk, which I would informally label “entropy.”

Being unified can be advantageous, unless it reflects group-think or social exclusivity that will prevent the organization from growing.

Once an organization knows its specific challenges, it can use appropriate programming to make progress. For instance, if the group is socially fragmented, maybe it needs more social opportunities. If it is polarized, maybe a well-chosen discussion could help produce more bridges. If it displays entropy, maybe it needs a formal strategic plan.

I would generally anticipate that bridges are helpful and should be supported and encouraged. In our study, all the data will be anonymous, so our partner will not know the identity of any people who bridge gaps. But a different application of this method could reveal that information.

Although I am focused on this study now, I remain open to partnerships with other organizations so that I can continue this research agenda. Let me know if you lead an organization that would like to do a similar study a bit later on.

Reference: Dalege, J., Galesic, M., & Olsson, H. (2023, April 12). Networks of Beliefs: An Integrative Theory of Individual- and Social-Level Belief Dynamics. See also: Analyzing Political Opinions and Discussions as Networks of Ideas; Mapping Ideologies as Networks of Ideas; seeking a religious congregation for a research study

the age of cybernetics

A pivotal period in the development of our current world was the first decade after WWII. Much happened then, including the first great wave of decolonization and the solidification of democratic welfare states in Europe, but I’m especially interested in the intellectual and technological developments that bore the (now obsolete) label of “cybernetics.”

I’ve been influenced by reading Francisco Varela, Evan Thompson, and Eleanor Rosch, The Embodied Mind: Cognitive Science and Human Experience (first ed. 1991, revised ed., 2017), but I’d tell the story in a somewhat different way.

The War itself saw the rapid development of entities that seemed analogous to human brains. Those included the first computers, radar, and mechanisms for directing artillery. They also included extremely complex organizations for manufacturing and deploying arms and materiel. Accompanying these pragmatic breakthroughs were successful new techniques for modeling complex processes mathematically, plus intellectual innovations such as artificial neurons (McCullouch & Pitts 1943), feedback (Rosenblueth, Wiener, and Bigelow 1943), game theory (von Neumann & Morgenstern, 1944), stored-program computers (Turing 1946), information theory (Shannon 1948), systems engineering (Bell Labs, 1940s), and related work in economic theory (e.g., Schumpeter 1942) and anthropology (Mead 1942).

Perhaps these developments were overshadowed by nuclear physics and the Bomb, but even the Manhattan Project was a massive application of systems engineering. Concepts, people, money, minerals, and energy were organized for a common task.

After the War, some of the contributors recognized that these developments were related. The Macy Conferences, held regularly from 1942-1960, drew a Who’s Who of scientists, clinicians, philosophers, and social scientists. The topics of the first post-War Macy Conference (March 1946) included “Self-regulating and teleological mechanisms,” “Simulated neural networks emulating the calculus of propositional logic,” “Anthropology and how computers might learn how to learn,” “Object perception’s feedback mechanisms,” and “Deriving ethics from science.” Participants demonstrated notably diverse intellectual interests and orientations. For example, both Margaret Mead (a qualitative and socially critical anthropologist) and Norbert Wiener (a mathematician) were influential.

Wiener (who had graduated from Tufts in 1909 at age 14) argued that the central issue could be labeled “cybernetics” (Wiener & Rosenblueth 1947). He and his colleagues derived this term from the ancient Greek word for the person who steers a boat. For Wiener, the basic question was how any person, another animal, a machine, or a society attempts to direct itself while receiving feedback.

According to Varela, Thompson, and Rosch, the ferment and diversity of the first wave of cybernetics was lost when a single model became temporarily dominant. This was the idea of the von Neumann machine:

Such a machine stores data that may symbolize something about the world. Human beings write elaborate and intentional instructions (software) for how those data will be changed (computation) in response to new input. There is an input device, such as a punchcard reader or keyboard, and an output mechanism, such as a screen or printer. You type something, the processor computes, and out comes a result.

One can imagine human beings, other animals, and large organizations working like von Neumann machines. For instance, we get input from vision, we store memories, we reason about what we experience, and we say and do things as a result. But there is no evident connection between this architecture and the design of the actual human brain. (Where in our head is all that complicated software stored?) Besides, computers designed in this way made disappointing progress on artificial intelligence between 1945 and 1970. The 1968 movie 2001: A Space Odyssey envisioned a computer with a human personality by the turn of our century, but real technology has lagged far behind that.

The term “cybernetics” had named a truly interdisciplinary field. After about 1956, the word faded as the intellectual community split into separate disciplines, including computer science.

This was also the period when behaviorism was dominant in psychology (presuming that all we do is to act in ways that independent observers can see–there is nothing meaningful “inside” us). It was perhaps the peak of what James C. Scott calls “high modernism” (the idea that a state can accurately see and reorganize the whole society). And it was the heyday of “pluralism” in political science (which assumes that each group that is part of a polity automatically pursues its own interests). All of these movements have a certain kinship with the von Neumann architecture.

An alternative was already considered in the era of cybernetics: emergence from networks. Instead of designing a complex system to follow instructions, one can connect numerous simple components into a network and give them simple rules for changing their connections in respond to feedback. The dramatic changes in our digital world since ca. 1980 have used this approach rather than any central design, and now the analogy of machine intelligence to neural networks is dominant. Emergent order can operate at several levels at once; for example, we can envision individuals whose brains are neural networks connecting via electronic networks (such as the Internet) to form social networks and culture.

I have sketched this history–briefly and unreliably, because it’s not my expertise–without intending value-judgments. I am not sure to what extent these developments have been beneficial or destructive. But it seems important to understand where we’ve come from to know where we should go from here.

See also: growing up with computers; ideologies and complex systems; The truth in Hayek; the progress of science; the human coordination involved in AI; the difference between human and artificial intelligence: relationships

Luca in the dog park

thinking both sides of the limits of human cognition

My dog knows many things about me, like whether I’m about to take him out for a walk and even what I mean when I say the words “dog park.” He has questions about me–for instance, when will I come home?–and sometimes gets answers. These are his “known unknowns.” He can let me know he has questions by cocking his head.

There are also some things he doesn’t know that he isn’t even aware of not knowing. For example, he’s allowed to run off-leash in the park because the city of Cambridge, Massachusetts has licensed him as a resident pet. That status is designated by the tag under his neck. He knows a lot about the park, and he’s aware of the tag (at least when it’s being put on him for the first time), but there’s no path to his understanding that a city is a political jurisdiction that derives power from the state to grant and withhold rights to dogs, which is why he’s running around in the grass.

To use the vocabulary pioneered by Jakob von Uexküll–which has been influential in very disparate intellectual traditions–my dog has an “umwelt,” a model of his world that is shaped, or perhaps “enacted,” by his biophysical characteristics (such as his sensitive nose and inability to speak) and their interactions with the objects he encounters (Varela, Thompson & Rosch 1991). I have a different umwelt, even though the two of us may be walking together through the same space at the same time. For me, we are in a city park, because I use words and concepts about social organization. For Luca, we are in a field luxuriously supplied with interestingly stinky smells and other dogs.

I know many things about Luca, such as his preference for the park over regular city streets. I know that his sense of smell is at least 10,000 times more acute than mine, and I can infer that he is much more interested than I am in the scents around the perimeter of the dog park because he derives far more information from them than I could. I could learn more about what specifically he smells there and even which chemical compounds are involved.

Some would say that I will never feel what it’s like to smell as well as he does. Others would reply that anything true about what he senses could be captured in my language and tested empirically by human beings, and it’s empty to say that we cannot know what he experiences.

I might have “unknown unknowns” about my dog. They could be unknown from my particular historical position, in the same way that people hundreds of year ago didn’t know to wonder about mammals’ neural networks. Or they could be permanently unknown to homo sapiens because we have a different experience from a dog’s and we don’t even know what to ask.

One view of that last statement is that it’s false, because dogs and people are highly similar. But what would we say about bats (Nagel 1974), or extraterrestrials with far bigger brains than ours? Maybe we miss aspects of their world, much as Luca misses the legal significance of the tag on his collar.

Another view is that talking about permanently unknown-unknowns is empty, or even nonsense. But nonsense is not necessarily bad for one’s character and state of mind. We might ask whether it is wise or foolish to reflect on the abstract possibility of thought beyond our capacity to think. A classic text for that discussion is the Preface to Wittgenstein’s Tractatus, where he says:

The book will, therefore, draw a limit to thinking, or rather—not to thinking, but to the expression of thoughts; for, in order to draw a limit to thinking we should have to be able to think both sides of this limit (we should therefore have to be able to think what cannot be thought).

The limit can, therefore, only be drawn in language and what lies on the other side of the limit will be simply nonsense.

Wittgenstein does not attempt to write about what lies beyond the limit because he does not write nonsense. But I think it remains debated whether he advises us to reflect on the limit “from both sides.” One way to do that would be to grasp and truly feel that we inhabit an umwelt that is not the same as the world in-itself–in other words, that there are things beyond our ken.

On one hand, I am a little suspicious of intimations about the actual nature of what lies beyond the line. I suspect that those vague ideas are generated by our very human hopes and fears and don’t represent signals from beyond our umwelt. On the other hand, I find it consoling that there is a limit to the field in which our sense can run (even with technical assistance), and that there must be much beyond it–just as a whole city begins outside the fence of our park.

This aphorism by Dogen (who lived 1200-1253 CE) suggests a similar idea:

Birth is just like riding in a boat. You raise the sails and you steer. Although you maneuver the sail and the pole, the boat gives you a ride, and without the boat you couldn’t ride. But you ride in the boat, and your riding makes the boat what it is. Investigate a moment such as this. At just such a moment, there is nothing but the world of the boat. The sky, the water, and the shore are all the boat’s world, which is not the same as a world that is not the boat’s. Thus you make birth what it is; you make birth your birth. When you ride in a boat, your body, mind, and environs together are the undivided activity of the boat. The entire earth and the entire sky are both the undivided activity of the boat. Thus birth is nothing but you; you are nothing but birth (p. 115).

References: Varela, Francisco J., Evan Thompson, and Eleanor Rosch. The embodied mind, revised edition: Cognitive science and human experience. MIT press, 2017; Nagel, Thomas. “What is it like to be a bat?” The Philosophical Review 83 (1974): 435-50; Wittgenstein, Ludwig, Tractatus Logico-Philosophicus, English trans. (London, 1922); The Essential Dogen: Writings of the Great Zen Master edited by Kazuaki Tanahashi and Peter Levitt, Shambhala 2013. See also: joys and limitations of phenomenology; let’s go for a walk