a collective model of the ethics of AI in higher education

Hannah Cox, James Fisher, and I have published a short piece in an outlet called eCampus News. The whole text is here, and I’ll paste the beginning here:

AI is difficult to understand, and its future is even harder to predict. Whenever we face complex and uncertain change, we need mental models to make preliminary sense of what is happening.

So far, many of the models that people are using for AI are metaphors, referring to things that we understand better, such as talking birds, the printing press, a monsterconventional corporations, or the Industrial Revolution. Such metaphors are really shorthand for elaborate models that incorporate factual assumptions, predictions, and value-judgments. No one can be sure which model is wisest, but we should be forming explicit models so that we can share them with other people, test them against new information, and revise them accordingly.

“Forming models” may not be exactly how a group of Tufts undergraduates understood their task when they chose to hold discussions of AI in education, but they certainly believed that they should form and exchange ideas about this topic. For an hour, these students considered the implications of using AI as a research and educational tool, academic dishonesty, big tech companies, attempts to regulate AI, and related issues. They allowed us to observe and record their discussion, and we derived a visual model from what they said.

We present this model [see above] as a starting point for anyone else’s reflections on AI in education. The Tufts students are not necessarily representative of college students in general, nor are they exceptionally expert on AI. But they are thoughtful people active in higher education who can help others to enter a critical conversation.

Our method for deriving a diagram from their discussion is unusual and requires an explanation. In almost every comment that a student made, at least two ideas were linked together. For instance, one student said: “If not regulated correctly, AI tools might lead students to abuse the technology in dishonest ways.” We interpret that comment as a link between two ideas: lack of regulation and academic dishonesty. When the three of us analyzed their whole conversation, we found 32 such ideas and 175 connections among them.

The graphic shows the 12 ideas that were most commonly mentioned and linked to others. The size of each dot reflects the number of times each idea was linked to another. The direction of the arrow indicated which factor caused or explained another.

The rest of the published article explores the content and meaning of the diagram a bit.

I am interested in the methodology that we employed here, for two reasons.

First, it’s a form of qualitative research–drawing on Epistemic Network Analysis (ENA) and related methods. As such, it yields a representation of a body of text and a description of what the participants said.

Second, it’s a way for a group to co-create a shared framework for understanding any issue. The graphic doesn’t represent their agreement but rather a common space for disagreement and dialogue. As such, it resembles forms of participatory modeling (Voinov et al, 2018). These techniques can be practically useful for groups that discuss what to do.

Our method was not dramatically innovative, but we did something a bit novel by coding ideas as nodes and the relationships between pairs of ideas as links.

Source: Alexey Voinov et al, “Tools and methods in participatory modeling: Selecting the right tool for the job,” Environmental Modelling & Software, vol 19 (2018), pp. 232-255. See also: what I would advise students about ChatGPT; People are not Points in Space; different kinds of social models; social education as learning to improve models

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People are not Points in Space

Newly published: Levine, P. (2024). People are not Points in Space: Network Models of Beliefs and Discussions. Critical Review, 1–27 (2024). https://doi.org/10.1080/08913811.2024.2344994 (Or a free pre-print version)

Abstract:

Metaphors of positions, spectrums, perspectives, viewpoints, and polarization reflect the same model, which treats beliefs—and the people who hold them—as points in space. This model is deeply rooted in quantitative research methods and influential traditions of Continental philosophy, and it is evident in some qualitative research. It can suggest that deliberation is difficult and rare because many people are located far apart ideologically, and their respective positions can be explained as dependent variables of factors like personality, partisanship, and demographics. An alternative model treats a given person’s beliefs as connected by reasons to form networks. People disclose the connections among their respective beliefs when they discuss issues. This model offers insights about specific cases, such as discussions conducted on two US college campuses, which are represented here as belief-networks. The model also supports a more optimistic view of the public’s capacity to deliberate.

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An Association as a Belief Network and Social Network

This is a paper that I presented at the Midwest Political Science Association on April 6, 2024. I hope to reproduce this study with another organization before publishing the results as a comparison. I am open to investigating groups that you may be involved with–a Rotary Club like the one in this study, a religious congregation, or something else. Please contact me if you are interested in exploring such a study.

Abstract

A social network is composed of individuals who may have various relationships with one another. Each member of such a network may hold relevant beliefs and may connect each belief to other beliefs. A connection between two beliefs is a reason. Each member’s beliefs and reasons form a more-or-less connected network. As members of a group interact, they share some of their respective beliefs and reasons with peers and form a belief-network that represents their common view. However, either the social network or the belief network can be disconnected if the group is divided.

This study mapped both the social network and the belief-network of a Rotary Club in the US Midwest. The Club’s leadership found the results useful for diagnostic and planning purposes. This study also piloted a methodology that may be useful for social scientists who analyze organizations and associations of various kinds.

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An Association as a Belief Network and Social Network

I will present a paper entitled “An Association as a Belief Network and Social Network” at next week’s Midwestern Political Science Association meeting (remotely). This is the paper.

Abstract:

A social network is composed of individuals who may have various relationships with one another. Each member of such a network may hold relevant beliefs and may connect each belief to other beliefs. A connection between two beliefs is a reason. Each member’s beliefs and reasons form a more-or-less connected network. As members of a group interact, they share some of their respective beliefs and reasons with peers and form a belief-network that represents their common view. However, either the social network or the belief network can be disconnected if the group is divided.

This study mapped both the social network and the belief-network of a Rotary Club in the US Midwest. The Club’s leadership found the results useful for diagnostic and planning purposes. This study also piloted a methodology that may be useful for social scientists who analyze organizations and associations of various kinds.

Two illustrative graphs …

Below is the social network of the organization. A link indicates that someone named another person as a significant influence. The size of each dot reflects the number of people who named that individual. The network is connected, not balkanized. However, there are definitely some insiders, who have lots of connections, and a periphery.

The belief-network is shown above this post. The nodes are beliefs held by members of the group. A link indicates that some members connect one belief to another as a reason, e.g., “I appreciate friendships in the club” and therefore, “I enjoy the meetings” (or vice-versa). Nodes with more connections are larger and placed nearer the center.

One takeaway is that members disagree about certain matters, such as the state of the local economy, but those contested beliefs do not serve as reasons for other beliefs, which prevents the group from fragmenting.

I would be interested in replicating this method with other organizations. I can share practical takeaways with a group while learning more from the additional case.

See also: a method for analyzing organizations

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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

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. https://doi.org/10.31219/osf.io/368jz. 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

when does a narrower range of opinions reflect learning?

John Stuart Mill’s On Liberty is the classic argument that all views should be freely expressed–by people who sincerely hold them–because unfettered debate contributes to public reasoning and learning. For Mill, controversy is good. However, he acknowledges a complication:

The cessation, on one question after another, of serious controversy, is one of the necessary incidents of the consolidation of opinion; a consolidation as salutary in the case of true opinions, as it is dangerous and noxious when the opinions are erroneous (Mill 1859/2011, 81)

In other words, as people reason together, they may discard or marginalize some views, leaving a narrower range to be considered. Whether such narrowing is desirable depends on whether the range of views that remains is (to quote Mill) “true.” His invocation of truth–as opposed to the procedural value of free speech–creates some complications for Mill’s philosophical position. But the challenge he poses is highly relevant to our current debates about speech in academia.

I think one influential view is that discussion is mostly the expression of beliefs or opinions, and more of that is better. When the range of opinions in a particular context becomes narrow, this can indicate a lack of freedom and diversity. For instance, the liberal/progressive tilt in some reaches of academia might represent a lack of viewpoint diversity.

A different prevalent view is that inquiry is meant to resolve issues, and therefore, the existence of multiple opinions about the same topic indicates a deficit. It means that an intellectual problem has not yet been resolved. To be sure, the pursuit of knowledge is permanent–disagreement is always to be expected–but we should generally celebrate when any given thesis achieves consensus.

Relatedly, some people see college as something like a debate club or editorial page, in which the main activity is expressing diverse opinions. Others see it as more like a laboratory, which is mainly a place for applying rigorous methods to get answers. (Of course, it could be a bit of both, or something entirely different.)

In 2015, we organized simultaneous student discussions of the same issue–the causes of health disparities–at Kansas State University and Tufts University. The results are here. At Kansas State, students discussed–and disagreed about–whether structural issues like race and class and/or personal behavioral choices explain health disparities. At Tufts, students quickly rejected the behavioral explanations and spent their time on the structural ones. Our graphic representation of the discussions shows a broader conversation at K-State and what Mill would call a “consolidated” one at Tufts.

A complication is that Tufts students happened to hear a professional lecture about the structural causes of health disparities before they discussed the issue, and we didn’t mirror that experience at K-State. Some Tufts students explicitly cited this lecture when rejecting individual/behavioral explanations of health disparities in their discussion.

Here are two competing reactions to this experiment.

First, Kansas State students demonstrated more ideological diversity and had a better conversation than the one at Tufts because it was broader. They also explicitly considered a claim that is prominently made in public–that individuals are responsible for their own poor health. Debating that thesis would prepare them for public engagement, regardless of where they stand on the issue. The Tufts conversation, on the other hand, was constrained, possibly due to the excessive influence of professors who hold contentious views of their own. The Tufts classroom was in a “bubble.”

Alternatively, the Tufts students happened to have a better opportunity to learn than their K-State peers because they heard an expert share the current state of research, and they chose to reject certain views as erroneous. It’s not that they were better citizens or that they know more (in general) than their counterparts at KSU, but simply that their discussion of this topic was better informed. Insofar as the lecture on public health found a receptive audience in the Tufts classroom, it was because these students had previously absorbed valid lessons about structural inequality from other sources.

I am not sure how to adjudicate these interpretations without independently evaluating the thesis that health disparities are caused by structural factors. If that thesis is true, then the narrowing reflected at Tufts is “salutary.” If it is false, then the narrowing is “dangerous and noxious.”

I don’t think it’s satisfactory to say that we can never tell, because then we can never believe that anything is true. But it can be hard to be sure …

See also: modeling a political discussion; “Analyzing Political Opinions and Discussions as Networks of Ideas“; right and left on campus today; academic freedom for individuals and for groups; marginalizing odious views: a strategy; vaccination, masking, political polarization, and the authority of science etc.

Analyzing Political Opinions and Discussions as Networks of Ideas

This is a talk that I have prepared for the Universidad Carlo III in Madrid tomorrow. It is a summary of recent work that I have been conducting with colleagues at Northeastern, Wisconsin, and Oxford and that I’m beginning to develop into a book manuscript.

In the model that I present, an individual holds potentially connected beliefs about political or moral issues, which we can represent with nodes and links (an “idiodictuon”). Whether and how the various ideas are linked in the person’s network influences that individual’s actions and opinions. When people discuss political or ethical issues, they share portions of their respective networks of which they are conscious at the time and may bring ideas from their interlocutors into their own idiodictuons.

Some network structures are better than others for discussion: overly centralized or scattered networks are problematic. Individuals tend to demonstrate similar network structures on different issues, so that having a proclivity for a certain form of network is a character trait.

People, with their respective networks of ideas, are also embedded in social networks. An idea is more likely to spread depending on features of both the social network and the idea networks of the people who interact. Specifically, the odds that an idea will spread from a given person depend on how many people receive communications from that person and how much they trust the communicator. It is reasonable to take into account the trustworthiness of a source when assessing an idea.

As a whole, a population may develop a shared network structure. An idea that is widely shared and frequently central in individuals’ networks becomes a norm. Such norms play important roles in institutions. A community or a culture is a single network or phylodictuon that encompasses disagreement. Ultimately, all such networks interconnect to form a network of human ideas.

against the idea of viewpoint diversity

In “People deserve safety on college campuses, ideas don’t,” Andrew J. Perrin and Christian Lundberg make an important argument against the idea of viewpoint diversity. They write:

Emphasizing viewpoint teaches students to not bother separating ideas from the people who hold them. Viewpoint is a visual metaphor that attaches what a person believes to where they sit: Viewpoints are properties people own and express, not ideas to be evaluated. It’s a classic ad hominem fallacy that renders argument fruitless.

We all draw on experience, and our experiences are influenced by our social positions. That is why demographic diversity is intellectually valuable. If, for example, men monopolize the conversation, then issues and solutions that are more obvious to other genders will probably be overlooked, or, at best, underplayed. The fact that some individuals demonstrate exceptional insight into others’ experiences does not negate this point. (See “Dear Mrs Amartya Sen, men will never understand us.”)

However, the metaphor of a viewpoint makes people’s ideas look like automatic functions of their social positions. It overlooks the diversity and freedom of individuals in any given social group; it makes reasoning and argument look fruitless; it implies that incorporating individuals with additional viewpoints will automatically improve a group and should be the main goal; and it suggests that a critical assessment of an idea is an attack on the person who holds it. As Perrin and Lundberg conclude:

Settling for exposure to viewpoints — as if they were infections to which one might develop antibodies — places them outside the realm of argument and reason. We fail those on the political left by ignoring conservative arguments instead of engaging them. Meanwhile, conservative students learn that their ideas are something others should be exposed to rather than meaningfully engaged.

I believe that the metaphor of a viewpoint is deeply rooted, and that challenging it could be quite fruitful. Put more generally, the image of a point in space is remarkably widely used to define people and ideas.

The most familiar example is the left-to-right political spectrum, which allows a person, an opinion or position, or a party or movement to be located at one point on a straight line. People or ideas can easily be visualized as points in two-dimensional space if they are located along two axes at once. For instance, Americans have often been described as liberal versus conservative on economics and on race, as two separate dimensions. Three dimensions are harder to depict on paper or a flat screen, although a three-dimensional model can be rotated and presented meaningfully on a plane. In any case, mathematics allows adding more than three dimensions, even though we can’t picture them visually, by simply tagging a given person, idea, or party or movement with many variables at once. Prevailing statistical methods, such as factor analysis, treat people, ideas, or groups as points in many-dimensional space and envision differences as the distance between positions. Many models try to explain why a person occupies a given point based on other known information about the same individual, such as party identification or race.

If a model employs many dimensions, it can incorporate any amount of quantitative data about the people and ideas being studied. Since each person or idea has a good chance of occupying a unique position in multidimensional space, there is relatively little danger that individuals will be casually lumped together in large groups.

However, some kinds of information must be lost in a model based on points in space. First, this metaphor conceals the way that ideas may connect to each other. If respondents are asked many questions on a survey, standard statistical methods capture correlations among their answers but cannot detect logical relationships among any individual’s ideas. Does a person believe one thing because of another belief, or despite it, or as two disconnected ideas? The structure of individuals’ thinking—if there is any—is lost. In contrast, when we read an impressive political argument or speech, we are primarily interested in its structure: in why (or whether) each point implies the next, or qualifies it, or contradicts it. A metaphor of points in space makes everyone look much more simple-minded than any careful speaker or writer.

To be sure, some of us probably fail to connect our separate ideas in reasonable ways, but we cannot know how many from standard survey research. The metaphor of points in space is biased against detecting complexity of thought, if there is any (Levine 2022).

Importantly, large bodies of research based on this model find that people are not responsive to arguments, that their beliefs are either incoherent or driven by indefensible biases, that they supply reasons after the fact to rationalize what they already desire—in short, that anything remotely resembling a deliberative democracy is psychologically naïve. Paul Sniderman—who dissents in interesting ways from what he calls “the textbook view of citizens’ capacity to reason about politics”—summarizes the consensus of his fellow political scientists as follows. “Average citizens’ knowledge about politics and public affairs is threadbare; their political beliefs minimally coherent, indeed, often self-contradictory; their support for core democratic values all too likely to crumble in the face of a threat, real or imaginary” (Sniderman 2017, pp. 42, 107).

Factor analysis is a statistical technique. It is often described as scientific, where “science” means a cumulative, empirical research project of testing hypotheses with data. Famous contributors to the statistical study of political opinions and behavior who have used a point-in-space model are English-speaking social and behavioral scientists like Charles Spearman, who invented factor analysis, and Philip Converse and his colleagues, who pioneered academic political survey research with the American National Election Studies.

A strangely similar metaphor is also influential in a very different tradition: Continental European political philosophy. Until the late 1800s, the words “culture” and “religion” had made sense only in the singular. People either had culture or not; they were either religious or not. But Romantic-Era thinkers began to see deep plurality. There were many cultures, religions, and nations (or peoples), understood as distinct in fundamental ways. These thinkers imagined that individuals saw the world from the perspective of their respective cultures or religions. Two people from different cultures would behold a different reality, although people who shared a culture would share a common worldview. A word for everything that can be seen from a given point is “horizon.” Perspective, viewpoint, and/or horizon were keywords in the thought of Herder, Hegel, Marx, Nietzsche, Heidegger, and many other highly abstract European philosophers.

Again, a person with a perspective occupies a point in space. This metaphor generates insights—there may be a French, or a modern, or a bourgeois perspective on certain topics—but it also obscures and creates conundrums. If people hold all their beliefs because of their fundamental perspectives or viewpoints, then a critique of any of their beliefs can be taken as an objection to the person and their right to speak. In that case, arguments about ideas can seem uncivil and even threatening.

Furthermore, if human beings are assigned to cultures on a “one-to-a-customer basis,” (Wolcott 1991, p. 247), and if each culture fundamentally shapes how all its members understand the world, then how can anyone know anything objectively, including the nature of other people’s cultures? Surely everything we think is relative to our perspective. Deep cultural relativism leads to basic skepticism or even nihilism, as Nietzsche most famously argued.

One way out is to argue that a fair institution is one that treats all cultures and religions equally and neutrally. For instance, the great American political philosopher John Rawls assumes “that a modern democratic society is characterized … by a plurality of reasonable but incompatible comprehensive doctrines” (Rawls 1993, pp. xvi, 59). Each of these doctrines determines each person’s values. Rawls concludes that a fair government must be neutral among these doctrines; indeed, he sees justice as fairness. Demands for “viewpoint diversity” on college campuses have a similar logic. However, critics have argued that neutrality is impossible (liberal institutions inevitably reflect specific values) and mere fairness among perspectives is an unsatisfactory account of justice.

Whether it is invoked in a statistical model or a work of political philosophy, a point in space from which one sees the world is a metaphor. It should not be taken too literally. We have other ways of describing the complexity of human interactions. We can model conversations as games with players and moves. We can envision ideas flowing through society on a hydraulic model, with pressure and viscosity (Allen 2015). Caroline Levine shows that literary writing often makes use of four forms—wholes, rhythms, hierarchies, and networks—to represent social phenomena (C. Levine 2015).

Indeed, we live in a period of fascination with networks: electronic, neural, social, semantic, and many other kinds. This means that we have powerful new techniques for analyzing networks, and many recent studies apply these techniques to people and ideas in ways that offer insights about politics.

This is why I have been working with colleagues to replace the metaphor of points in space with one of networks. I have introduced the technical term idiodictuon for the network of ideas that each individual holds, where the connections among ideas are reasons.

In this model, when people discuss issues, they are sharing ideas and connections that others may choose to incorporate into their respective idiodictuons. Whether we encounter another person’s ideas depends on whether we are connected to that person in some kind of social network. Human beings who discuss within a network of relationships form a phylodictuon (a shared network of ideas, including ones that conflict).

It is generally good for a phylodictuon to encompass diverse ideas and ideas from diverse people (which are different matters), yet the job of a wise community is to improve its collection of ideas and how they are organized, not merely to ensure that all available ideas are included. As Perrin and Lundberg write, “Confronting serious ideas means that while every person deserves safety on campus, no idea does; all ideas deserve the respect that a real stress test brings.”

See also: individuals in cultures: the concept of an idiodictuon; Mapping Ideologies as Networks of Ideas; a mistaken view of culture; Teaching Honest History: a conversation with Randi Weingarten and Marcia Chatelain; etc.

Sources: Perrin, Andrew J. and Christian Lundberg, “People deserve safety on college campuses. Ideas don’t,” The Boston Globe, March 29; Paul M. Sniderman, The Democratic Faith: Essays on Democratic Citizenship (Yale University Press); Peter Levine, “Mapping ideologies as networks of ideas,” Journal of Political Ideologies, 2022, DOI: 10.1080/13569317.2022.2138293; Harry F. Wolcott, “Propriospect and the acquisition of culture., Anthropology & Education Quarterly 22, no. 3 (1991): 251-273; John Rawls (Political Liberalism. New York: Columbia University Press, 1993); Danielle Allen, “Reconceiving Public Spheres: The Flow Dynamics Model,” in Allen and Jennifer S. Light, From Voice to Influence: Understanding Citizenship in a Digital Age, University of Chicago Press, 2015, pp. 178-207; Caroline Levine, Caroline, Forms: Whole, Rhythm, Hierarchy, Network (Princeton University Press, 2015).