2 Theoretical background

2.1 The Hegselmann–Krause model

The HK model is among the most popular frameworks in the domain of agent-based computational modeling. While the model admits of a variety of interpretations (see, e.g., Hegselmann Reference Hegselmann2023), it is most commonly interpreted to encapsulate the interplay between two key aspects of human epistemic behavior: the assimilation of information from social peers and the direct acquisition of knowledge from empirical evidence. On this interpretation, it serves as a mathematical abstraction of opinion dynamics, where agents iteratively adjust their beliefs about the value of some parameter $\tau \in \left[ {0,1} \right]$ , whose meaning remains unspecified.

Formally, each agent $i$ starts at time 0 with an estimate ${x_i}\left( 0 \right)$ of $\tau $ and revises this estimate over discrete time steps, where at each time $t$ the revision process is influenced by two primary factors: evidence about $\tau $ the agent receives directly from the world at $t$ and the opinions of its peers at $t$ , which are formally defined to be the agents within its bounded confidence interval (BCI) at $t$ , that is, whose estimates of $\tau $ at $t$ differ by no more than some small value $\varepsilon $ from the agent’s own estimate at $t$ . Then agent $i$ ’s opinion concerning $\tau $ after the $\left( {n + 1} \right)$ st update (i.e., at time $n + 1$ ) is defined to be

$${x_i}\left( {n + 1} \right) = {{1 - \alpha } \over {\left| {{X_i}\left( n \right)} \right|}}{\rm{\;}}\mathop \sum \limits_{j \in {X_i}\left( n \right)} \,{x_j}\left( n \right)+ \alpha \tau, $$

with ${x_j}\left( n \right)$ being the opinion of agent $j$ after update $n$ and

$${X_i}\left( n \right): = \left\{ {\,j:\left| {{x_i}\left( n \right) - {x_j}\left( n \right)} \right| \le \epsilon {\rm{\;}}} \right\}$$

the set of agents within agent $i$ ’s BCI after update $n$ . The parameter $\alpha \in \left[ {0,1} \right]$ balances the weight given to social versus evidential information. (For illustrations, see the supplementary materials.)

A key virtue of the HK model is that it can be easily extended or adapted for the purpose of addressing specific research questions. For instance, researchers have explored scenarios with “noisy” evidence, where agents receive imperfect signals from the world (Douven Reference Douven2010), and have considered agents with interval-valued beliefs to account for vagueness (Crosscombe and Lawry Reference Crosscombe and Lawry2016) as well as agents that can simultaneously hold beliefs about multiple issues (Jacobmeier Reference Jacobmeier2004; Lorenz Reference Lorenz and Helbing2008; Pluchino, Latora, and Rapisarda Reference Pluchino, Latora and Rapisarda2006).

In line with the general critique of ABMs cited in the introduction, one could argue that the HK model, and even the aforementioned extensions of the model, features agents whose intellectual capacities are, for all we know, unrealistically impoverished. Proponents of the HK model could respond that this does not mean their model cannot be descriptively adequate at the macro-level, for instance, in predicting when a community of agents will reach a consensus and when it will not. While that is true, we believe that a more productive, and independently interesting, response is to consider ways to make the HK model more realistic. One way is to endow the agents in the model with something like a brain that is capable of learning much in the manner in which we humans learn. This is the approach to be taken here.

3 HK updating for neural networks

The key elements of the HK model are the notion of peerhood, regulated by the $\varepsilon $ parameter, and the operation of mixing worldly and social information, where the exact mixture depends on the value of the $\alpha $ parameter. We want to retain both elements in our new model, but, given that our agents are going to be MLPs, these elements need to be adapted.

In the original HK model, every agent is, at every point in time, fully characterized by its estimate of $\tau$ , making a definition of peerhood in terms of similarity of opinion the only plausible option. Accordingly, the model lets agents $i$ and $j$ be each other’s peers at $t$ precisely if $\left| {{x_i}\left( t \right) - {x_j}\left( t \right)} \right| \le \varepsilon $ . But with MLPs as agents, one agent can be similar to another agent in more than one respect. Most notably, while in the original HK model, there is no meaningful distinction between an agent’s state at a given point in time and its output (i.e., its estimate of $\tau $ ) at that point in time, in the new model, there is. At any point in time, an agent is in a certain state, fully characterized by its parameters (its weights and biases) at that time (architecture, including activation functions, will always be the same for all agents in a community), but it can also be characterized by its output (i.e., the predictions it would then make, if prompted). As a result, we can distinguish between state-based similarity and output-based similarity—and of course, agents can be similar to each other in both respects at the same time, which would make them state- and output-based similar.

Because MLPs can be used for various purposes, the output of an MLP can be many things: a single number, as in the HK model, or a grouping of items of interest into different classes (if the MLP is a classifier), or an assignment of probabilities to a set of competing hypotheses (e.g., if the MLP is used for a multinomial regression task), and so on. How to make the notion of output-based similarity precise will depend on the type of output we are dealing with. If, for instance, it is a single number, output-based similarity could again be defined in terms of absolute difference, as in the HK model. If the MLP is a classifier, one can consider a number of different metrics of classification similarity, such as the mutual information index, which we will use in the first study to be reported in this article; if the output is a probability distribution, then again, a number of options are available, such as the Kullback–Leibler (KL) divergence or the Jensen–Shannon (JS) divergence, the latter of which we will use in the second study; and so on. Two agents will then be said to be each other’s output-based peer precisely if they are close enough to each other in terms of the appropriate criterion.

The notion of state-based similarity requires more explanation. Given that, in our models, all agents (i.e., MLPs) will have the same architecture—the same number of layers, corresponding layers having the same number of nodes, corresponding nodes having the same activation function—we can measure their similarity by comparing their parameters, node per node. A common metric for this purpose is the cosine similarity, which requires that we vectorize the parameters first.Footnote ² Gathering, in some order, the weights and biases of agent $i$ in a vector ${\rm{param}}{{\rm{s}}_i}$ and proceeding analogously for the weights and biases of agent $j$ , obtaining ${\rm{param}}{{\rm{s}}_j}$ , their cosine similarity is calculated as

$${\rm{cossim}}\left( {i,j} \right)= {{{\rm{param}}{{\rm{s}}_i} \cdot {\rm{param}}{{\rm{s}}_j}} \over {\left\| {{\rm{param}}{{\rm{s}}_i}} \right\| \times \left\| {{\rm{param}}{{\rm{s}}_j}} \right\|}},$$

which will be a value between –1 and 1, with 1 indicating maximum similarity and –1 maximum dissimilarity.

To illustrate, consider the MLPs shown in figure 1. We lay out sequentially the parameters of each network from top to bottom and from left to right, and we calculate the dot product of the resulting vectors:

$$0.11 \cdot 0.87+1.26 \cdot 0.64+ 0.43 \cdot 1.22+ \cdots + 0.94 \cdot 0.81\approx 7.35.$$

Figure 1.

Multilayer perceptrons sharing the same architecture but with different weights and biases. (Weights are annotated on the edges connecting the neurons; biases appear inside the neurons).

We further calculate that the norm of the first vector equals

$$\sqrt {{{0.11}^2} + {{1.26}^2} + {{0.43}^2} + \cdots + {{0.94}^2}} \approx2.94$$

and that the norm of the second vector equals

$$\sqrt {{{0.87}^2} + {{0.64}^2} + {{1.22}^2} + \cdots + {{0.81}^2}} \approx 3.09.$$

Thus the cosine similarity for the aforementioned MLPs equals (approximately) $7.35/\left( {2.94 \times 3.09} \right) \approx 0.81$ .

We will say that agents $i$ and $j$ are state-based peers precisely if $cossim\left( {i,j} \right) \ge 1 - \varepsilon $ , for the chosen $\varepsilon \in \left[ {0,1} \right]$ ; so, for instance, if $\varepsilon = 0.2$ , then the agents in the preceding illustration are each other’s peer. Note that, as in the original HK model, a larger value of $\varepsilon $ means a more liberal or inclusive notion of peerhood, which does not require agents to be as similar with respect to their parameters to qualify as peers; conversely, the smaller the value of $\varepsilon $ is, the more similar the agents have to be, with the limiting case of $\varepsilon = 0$ meaning that the agents must be maximally similar, also analogous to the original HK model.

It merits emphasis that being able to differentiate types of peerhood—based on state, outcome, or a combination of the two—is already an enrichment compared to the original HK model. For, as social scientists have shown (e.g., Eysenbach et al. Reference Eysenbach, Powell, Englesakis, Rizo and Stern2004; Laninga-Wijnen and Veenstra Reference Laninga-Wijnen, Veenstra and Halpern-Felsher2023), in real life, peer selection is influenced by a multitude of criteria: we may want to team up with people who share our views but also with people who look like us or have the same educational background or socioeconomic status. State-based peers could be regarded as corresponding somewhat to peers who “look like us,” output-based peers as corresponding to peers who “have views like ours.”

The averaging operation can take different forms as well, again because, with MLPs as agents, we can make a state–output distinction. Supposing we have determined an agent’s peers at a given point in time (be these state-based, output-based, or state- and output-based peers), one plausible option is to average the parameters of those peers and calculate the output of the network with the resulting averages as parameters, given the input at the point in time; another, equally plausible option is to calculate the outputs of all peers at the point in time and average those outputs. In general, the results will be different. Suppose, for instance, that the two MLPs depicted in figure 1 are both given as input the vector $\left( {2/3,1/3} \right)$ . Then it is an easy (if somewhat tedious) exercise to calculate that the left MLP will give as output (approximately) 3.70 and the right one will give as output (approximately) 5.33, yielding an average of (approximately) 4.52. But applying the procedure of the first option to the same MLPs results in the network shown in figure 2, and this network yields (approximately) 4.46 when given $\left( {2/3,1/3} \right)$ as input.Footnote ³

Figure 2.

Multilayer perceptron with weights and biases resulting from averaging the corresponding weights and biases from the multilayer perceptrons shown in figure 1.

The different definitions of peerhood and averaging can be combined in a variety of ways to obtain NABMs whose agents update in a HK-like fashion. We will make no attempt to be exhaustive here and confine ourselves to studying three models that could all be plausibly regarded as extensions of the HK model, the main difference in all three cases being that the traditional HK agents have been replaced by MLPs. Roughly, the first model assumes a state-based notion of peerhood and also averages agent parameters instead of outputs. The second model assumes an output-based notion of peerhood and averages outputs. And the third model combines the first and second, which means that it proceeds by averaging parameters of state-based peers but also by averaging outputs of output-based peers. In the remainder of this section, we describe each of the models in more detail, and in the next two sections, we use computer simulations to compare their performance on standard machine learning benchmarks.

All three models require as input a community of agents, which will be MLPs but could also be different types of networks; input data, split into a training and a test set; and values for parameters regulating peerhood and the mixing of worldly and social factors in updating. There is no restriction on the exact architecture of the MLPs, except that (1) it must be the same for all MLPs in a given community, meaning that they must have the same number of layers and that corresponding layers must have the same number of nodes as well as the same activation function, and (2) the input and output layers must (of course) fit the data and task, respectively. In the first two models to be considered, the parameter $\varepsilon $ regulates the criterion for peerhood (which, however, means different things in the two models), and the parameter $\alpha $ regulates the weighting of the worldly versus the social factor in updating (the weighing operation also means different things in the two models). The third model, which, as said, combines the first two, has two parameters regulating peerhood—one regulating state-based peerhood ( ${\varepsilon _1}$ ), the other output-based peerhood ( ${\varepsilon _2}$ )—as well as two weight parameters, one pertaining to the weighing of states ( ${\alpha _1}$ ), the other pertaining to the weighing of outputs ( ${\alpha _2}$ ).

The first model consists of three main parts. The first part calculates a cosine similarity matrix for all agents in the community and, on that basis, selects peers for each agent (i.e., the agents that are $\varepsilon $ -similar to it). It then calculates the averages of those parameters, in the way illustrated previously, and it stores these parameter averages. The second part, which can be thought of as the worldly part of the updating process, trains for one training round every agent (i.e., MLP) on the data it received, where it is left open at this point whether all agents receive the same data or receive different (possibly partly overlapping) subsets of the data. The third part, finally, takes a weighted average of the parameters of the agent that resulted from the training process in the second part and the parameter averages of the agent’s peers that were calculated in the first part, the weighing depending on the value of $\alpha $ . The parameters that result from this weighted averaging are then set as the new parameters of the agent. Algorithm A.1 in appendix A.2 presents pseudo-code for the updating method defined by this model. In that presentation, the procedure outputs both the updated agents and the results from evaluating the updated agents on the relevant data (the training set, or the test set, or both, whichever is most useful for one’s purposes).Footnote ⁴

The building blocks of the second model are basically the same as those of the first model, but they appear in a different order. First, all agents are trained on whatever the relevant data are (where it is again left open whether all agents are trained on the same data or whether each agent receives its own data set); then they make predictions, whether for their training data, their test data, or both (e.g., if the task at hand is one of classification, they predict, after being trained, how each data point will be classified); in a next step, the peers of each agent are determined on the basis of how similar their predictions are (the similarity cutoff depending on $\varepsilon $ ); and finally, some $\alpha $ -weighted average of the agent’s predictions after the worldly update and the averaged predictions of its peers is calculated and then evaluated. Algorithm A.2 presents the pseudo-code for the second model. As presented there, the procedure gives the result of the final step (i.e., of the weighted averaging) as output, together with the updated agents.

The notion of averaging, as it is used in the second model, requires a comment. Parameters are always numbers, and we know what it means to average numbers. So, in the first model, averaging always means taking the arithmetic average of whatever the relevant numbers are. But as already explained, given the many kinds of tasks MLPs can fulfill, the outputs in the case of the second model need not be numeric. As a result, the operations of averaging and weighted averaging, as carried out in the model, may differ, depending on the nature of the data or the task at hand. For instance, in the first study, averaging consists in determining the modal responses for the various data points, in a way to be detailed. Nevertheless, the intended meaning of averaging in this algorithm should be clear: the average is always some kind of best compromise of whatever different responses are under consideration.

The third model combines the previous two. Specifically, it proceeds as follows: (1) for each agent, select its state-based peers (depending on ${\varepsilon _1}$ ) and take the averages of their parameters; (2) train all agents on their training data; (3) for each agent, set its parameters equal to a weighted average of its parameters after the training and the averaged parameters of their state-based peers (before training; the weighting of the average is determined by ${\alpha _1}$ ); (4) let all agents make predictions on the relevant data; (5) for each agent, select its output-based peers (based on ${\varepsilon _2}$ ), in light of the predictions obtained in the previous step; and finally, (6) for each agent, take an ${\alpha _2}$ -weighted average of its own predictions and the averaged predictions of its output-based peers.

While in the description of the models we have not explicitly referred to an equivalent of the parameter $\tau $ in the HK model, the references to agents making predictions and being evaluated all refer implicitly to such an equivalent, that is, a target that the agents are aiming at and can get right to differing degrees; also, the data will, ideally, be informative of that equivalent, meaning that they will help the agent approximate the target, or even hit it. But precisely because MLPs can be used for a variety of purposes, it is impossible to characterize the target generally. If the MLPs are trained on a classification task, the aim is to classify correctly whatever data they are given as input; their predictions concern the classification of those data—they are their best guess of how the data are classified in reality—and they are evaluated in light of how closely their predictions match the correct classification, and similarly if the MLPs are trained to assign probabilities to a set of rival theories, or to predict time series, or to encrypt data. In all those cases, there is a target to which they are trying to come as close as possible and with respect to which they can be evaluated, but the nature of the target is different each time, unlike in the HK model, where it is always a number (or a set of numbers, in some extensions).

It is again to be noted that we are not aiming at exploring all possibilities of integrating the HK model with ANNs. We do believe, however, that the three models defined in the foregoing paragraphs are all natural extensions of that model as originally conceived, the new characteristic element being that the communities of agents are constituted by neural networks. While it has been shown that, depending on an agent’s environment and its goals (epistemic or otherwise) in that environment, social updating in the manner of the HK model can have notable epistemic advantages (Crosscombe and Lawry Reference Crosscombe and Lawry2016; Douven Reference Douven2010, Reference Douven2019; Douven and Hegselmann Reference Douven and Hegselmann2021, Reference Douven and Hegselmann2022; Glass and Glass Reference Glass and Glass2021), it remains to be seen whether there is any merit to HK updating for agents conceived as neural networks. To find out, the next two sections test the three models on tasks for which neural networks have been commonly used, and we compare the performance of networks in the models with that of neural networks carrying out the same tasks in a strictly individual fashion.

4 Study 1: Classifying colors

The first study considers communities of agents (i.e., MLPs) that are trained to classify colors on the basis of their coordinates in color similarity space, specifically CIELUV space (see figure A.1 [left] in appendix A.3; also see, for theoretical background, Fairchild Reference Fairchild2013). Both the training and the testing materials come from the 320 chromatic Munsell chips that served as the materials for the World Color Survey (WCS; Cook, Kay, and Regier Reference Cook, Kay and Regier2005), a large catalog of color-naming systems from across the globe; the 320 chips are highlighted figure A.1 (right) and shown in a chart in the way they were presented in the WCS in figure A.2.

Because a significant number of participants in color-naming studies for both English and French used only ten of the eleven basic color terms (“green,” “blue,” etc.) in describing the colors of the WCS chips, leaving out “gray” (Berlin and Kay Reference Berlin and Kay1969; Claidière, Jraissati, and Chevallier Reference Claidière, Jraissati and Chevallier2008), we take as the target classification that the agents should try to learn—our $\tau {\rm{\;\;\;\;}}$ , so to speak—a clustering of the WCS chips into ten categories. Also, because in the same color-naming studies, there was considerable interpersonal variability in how these chips were named, we use the $k$ -means clustering algorithm to provide a kind of objective approximation of the natural color concepts.Footnote ⁵ The result, which is the classification the agents should try to learn, is shown in figure A.3 (top).

The MLPs that populate the models are not much more complicated than the ones used in our earlier illustration. They also have only one hidden layer, now consisting of nine nodes and integrating the ReLU activation function for each node.Footnote ⁶ Given that the task at hand is to categorize colors as belonging to one of ten classes on the basis of their CIELUV coordinates, the input layers of the MLPs have three nodes—one for each coordinate—and the output layers ten, each representing one of the basic colors minus gray.

We ran three sets of simulations, one for each of the models defined in the previous section, in which the communities always consisted of fifty MLPs with the architecture described earlier. Each simulation involved training the agents over one hundred epochs, where an epoch is a single application of the given model, with the agents being returned after epoch $n$ serving as input for the model in epoch $n + 1$ , for $n \in \left\{ {1, \ldots, 99} \right\}$ .Footnote ⁷ The training used the Adam optimization algorithm (with a learning rate set to 0.001) and the multiclass cross-entropy loss, which computes the loss by measuring the difference between the predicted classification probabilities (i.e., the probability that a chip should be classified as green, the probability that it should be classified as blue, etc.) and the true class labels.

Per epoch, the agents received a fresh batch of training data, each time sampled randomly and for each agent individually from the WCS chips in such a way that the number of chips from each category according to the target classification was greater than 0 but otherwise random. Thus every agent was assigned at the beginning of each epoch a set ${\{\langle \langle L_c^{\rm{*}},u_c^{\rm{*}},v_c^{\rm{*}}\rangle, {C_c}\rangle\} _{c{\rm{\;}} \in {\rm{\;}}8}}$ of pairs as training data, with each pair comprising the CIELUV coordinates of some WCS chip $c$ in sample $s$ as well as its label ${C_c}$ indicating the color it has according to the target classification.Footnote ⁸ The test data on which the agents were evaluated after each epoch were always the same for all agents and consisted of the coordinates of all 320 color chips together with their labeling according to the target classification.

The evaluation used the mutual information index, which measures the similarity of different classifications (see, for why this measure is preferable to alternative measures, such as the Rand index, Pfitzer, Leibbrandt, and Powers Reference Pfitzer, Leibbrandt and Powers2009).Footnote ⁹ To be more precise, after each epoch, we measured the accuracy of each agent by calculating the mutual information between how it classified the 320 chips in our materials and how these chips ought to be classified according to the target classification.

For the first two models, which have only one $\varepsilon $ and one $\alpha $ parameter, we used a grid search strategy to approximate optimal combinations of parameters. For each combination resulting from letting $\varepsilon $ and $\alpha $ range independently over the unit interval in steps of 0.025, we ran one hundred simulations as described earlier. For state-based social updating, the parameter setting $\alpha = \varepsilon = 0.9$ yielded, on average, the highest mutual information at the end of the training process. For output-based social updating, the combination of $\alpha = 0.1$ and $\varepsilon = 0.3$ did best. For the combined social updating method, which has four parameters, a grid search would have been computationally too costly, and therefore we ran a random search procedure to approximate the best setting (or a best setting; uniqueness is not guaranteed). Specifically, we ran one hundred simulations for five hundred combinations of random choices (all uniformly sampled from the unit interval) for the two $\alpha $ and the two $\varepsilon $ parameters, finding that the best score (i.e., the highest average mutual information) after one hundred epochs was obtained for the setting ${\alpha _1} = 0.81$ , ${\varepsilon _1} = 0.9$ , ${\alpha _2} = 0.14$ , and ${\varepsilon _2} = 0.07$ .Footnote ¹⁰

To get a first impression of the accuracy that can be achieved using the different updating mechanisms with their optimal parameter setting, we trained a community of fifty agents for one thousand epochs for each of these mechanisms and compared the resulting modal classifications with the target classification. (A modal classification is the classification that gives, for each chip in our materials, the modal—that is, the most frequent—response for that chip in the given community.) For completeness, we included in the comparison the modal classification obtained from a community of fifty agents (MLPs) that do not engage in any social updating but are individually trained in the exact same way as the agents in the communities of social updaters are. It turned out that, first, the different updating methods led to modal classifications that looked almost the same and, second, that those classifications were almost identical with the target classification (see figure A.3). Indeed, a comparison with the target classification yielded the same high mutual information of 0.97 for each modal classification.

Should we conclude that the various forms of social updating are equally good but also that social updating, in whichever form, is not worth the extra effort of averaging (whether parameters or predictions, let alone both)? That would be rash, because the modal classifications tell a very incomplete story, for two reasons. First, note that modal responses can be the same even if, for one algorithm, only a small fraction of agents got the label right at the end (but wrong responses were all over the place), while for another, all, or almost all, agents got it right. Second, we will want to look at more than the end state of the training process and will also be interested in how fast the agents were able to learn. Perhaps all updating methods led to an excellent classification eventually, but if one already got the classification more or less right quite early on in the training process, while the other updating methods did not, then for many practical purposes, that will make the former preferable.

On these issues, figure 3 offers some helpful insights. For each of the four communities of agents under consideration (i.e., the community of nonsocial updaters and the three communities of social updaters, each using a different updating method with their optimal setting) and for each epoch, figure 3 shows the mean mutual information obtained by the agents, together with 95 percent confidence bands. We see that the combined state-based and output-based procedure swiftly surpasses the others, maintaining its lead throughout the training process.

Figure 3.

Per-epoch average mutual information (with 95 percent bootstrap confidence intervals) for the four communities of agents (social updating always with optimal settings; see the text). Effect sizes ( ${\omega ^2}$ ) for the ANOVAs that were run for each epoch are shown on the alternative $y$ -axis. SB, state based; OB, output based. (Color online.)

We conducted one-way analyses of variance (ANOVAs) for the mutual information scores of the four groups after each epoch. The ${\omega ^2}$ -values for each ANOVA are plotted on the alternative $y$ -axis of figure 3, a green marker indicating that the ANOVA showed group means to be significantly different, a red marker that they were not significantly different. An ${\omega ^2}$ -value greater than 0.14 is conventionally taken to indicate a large effect size, meaning in our case that, although the modal classifications of all communities were equally good at the end, even at the end, there were large differences among the communities in terms of average mutual information scores (i.e., how well, on average, members of the communities did with respect to approximating the target classification).

Results of the per-epoch follow-up tests with pairwise comparisons, which are contained in the supplementary materials, further reveal that not only does the combined updating method top all of its rivals after virtually all epochs but at almost every epoch, a choice of the former over any of the alternatives would largely impact the achieved accuracy (where the effect size was measured using Cohen’s $d$ ). The only method that at times comes close and sporadically even does better is the output-based social updating method. The pairwise comparisons also confirm what could already be guessed on the basis of figure 3, namely, that all social updating methods outperform individual updating by far.

We can also measure the total accuracy achieved by the agents over the one thousand epochs by using the area under the learning curve (AULC; see, e.g., Bouckaert Reference Bouckaert, Sattar and Kang2006; Tsai, Ho, and Lin Reference Tsai, Ho and Lin2010), which plots the learning curve of a neural network and measures the area under that curve. Networks that learn faster and achieve greater accuracy sooner will have a larger area under the learning curve, while models that learn more slowly or achieve a lower level of accuracy will have a smaller area, assuming the same number of epochs. Thus the AULC can be interpreted as a measure of the overall performance of the network throughout the training process, with larger values for this metric indicating better average performance of a network throughout the training process.

The AULC values obtained for the agents in the community using the combined social updating method were significantly larger than those obtained for the agents in the other communities. Specifically, a one-way ANOVA showed that type of updating had a significant and very large effect on AULC values over 1,000 epochs, $F\left( {3,196} \right) = 200.58$ , $p \lt 0.0001$ , ${\omega ^2} \!= 0.75$ . Pairwise $t$ -tests showed that the AULC values for the agents using combined social updating significantly exceeded those for the agents using any other method of updating (smallest $t = 52.04$ , all $p{\rm{s}} \lt 0.0001$ ), with a mean AULC value for the combined method of 938.40 ( $\!\pm $ 0.56), for the output-based method of 921.54 ( $\!\pm $ 2.22), for the state-based method of 897.00 ( $\!\pm $ 0.90), and for the nonsocial method of 866.95 ( $\!\pm $ 31.44). A Cohen’s $d$ test showed that using the best method (combined updating) instead of its closest competitor (output-based updating) still has a large impact on overall accuracy ( $d =\!10.41$ ).

5 Study 2: Staging hypertension

The setup of the second study is broadly the same as that of the first. We look at communities of agents that aim at a target and receive evidence relevant to that target. In this study, too, the communities use different update methods, with one community consisting of nonsocial updaters and three communities consisting of social updaters, one for each of the three models from section 3.

The target is different in this study. For ease of interpretation, imagine the agents to be medical interns tasked with predicting the stages of hypertension in patients based on a variety of demographic and lifestyle data, intentionally excluding direct blood pressure readings. For the training process, we use data sourced from the National Health and Nutrition Examination Survey (NHANES), which is a yearly survey conducted by the National Center for Health Statistics.Footnote ¹¹ From an initial cohort of 613 patients, we focus on the 587 adults aged twenty years and older. As key variables for analysis, we include age, gender, body mass index (BMI), diabetic status, physical activity, alcohol consumption, and smoking behavior. These variables present a mix of continuous (such as age, BMI, and physical activity), binary (gender), and ordinal (diabetic status, alcohol use, smoking) types. On the basis of these variables, the interns are to predict class probabilities for hypertension stages, ranging from normotensive (i.e., normal blood pressure), via prehypertensive and stages 1 and 2 hypertensive, to hypertensive crisis, thus encompassing five distinct categories. Hypertension stages were determined using the systolic and diastolic blood pressure readings included in the NHANES data set.

The agents are again modeled as MLPs, now comprising two hidden layers with thirty-two and sixteen nodes, respectively, employing the ReLU activation function. The input layer is designed to match the seven input variables (age, gender, etc.), while the output layer consists of five nodes corresponding to the hypertension stages. We use the softmax function in the output layer to model the output as a probability distribution, ensuring that the nodes’ outputs all lie between 0 and 1 (inclusive) and that their sum equals 1.

In the training process, an agent processes data from one patient at a time and assigns, on the basis of these data, probabilities to each of the relevant hypotheses (i.e., that the patient is normotensive, that she is prehypertensive, etc.). As in the first study, the training uses multiclass cross-entropy loss and the Adam optimization algorithm (with a learning rate of 0.005). This process represents the worldly part of the update, which for one community of agents is all the updating in which they engage. Three other communities of agents also participate in social updating, each using a distinct update method introduced in section 3.

As noted in section 3, state-based peers are always selected on the basis of the same criterion—namely, similarity of weights and biases—but the criterion on whose basis output-based peers are selected depends on the type of output generated. In the present case, the output consists of probability functions, and so we need a similarity measure for such functions. A prominent one is the KL divergence, but here we use the Jensen–Shannon (JS) divergence, ${D_{{\rm{JS}}}}$ , which is based on the KL divergence but which, unlike the latter, is symmetric, bounded, and normalized. Not only does that make it easier to interpret (0 indicates that the probability functions are identical, 1 that they are maximally different) but it can also be bounded by an $\varepsilon $ parameter whose value lies in the unit interval. Thus, where one agent’s predicted probabilities at a given point in time are represented by $p$ and another’s by $q$ , they will be said to be each other’s peer at that time precisely if ${D_{{\rm{JS}}}}\left( {p\parallel q} \right) \lt \varepsilon $ , for some specified $\varepsilon \in \left[ {0,1} \right]$ .

Relatedly, and as also previously explained, averaging of outputs will mean different things depending on the nature of the outputs. Here they are probability functions, and we use the best-known method for averaging such functions, which is linear pooling. Given probability functions $\{ {f_i}\} _{i = 1}^{n}$ , the weighted linear average of these functions is defined to be $\mathop \sum \nolimits_{i = 1}^{n} \,\,{\omega _i}{f_i}$ , with ${\omega _i} \ge 0$ for all $i$ and $\mathop \sum \nolimits_{i = 1}^{n} \,{\omega _i} = 1$ (see, e.g., Dietrich and List Reference Dietrich, List, Hájek and Hitchcock2016). In our model, peers are always weighted equally, meaning that we always take the straight average of their probability functions.

After each update, the agents are evaluated on the patients in the test set. Because they are making probabilistic predictions about these patients, and given that the hypotheses to which the probabilities get assigned are ordered (e.g., stage 1 hypertension is closer to stage 2 hypertension than the prehypertensive stage is), we evaluate the agents using the ranked probability score (RPS; see Epstein Reference Epstein1969). This scoring rule is particularly suited for the kind of case at hand, given that it penalizes predictions not only on the basis of how much they differ from the objective probabilities but also on the basis of the “distance” between the hypotheses in terms of their order. For example, if an agent incorrectly assigns a high probability to a stage that is adjacent to the true stage (e.g., assigning a high probability to the hypothesis that the patient has stage 1 hypertension when the patient actually has stage 2 hypertension), this is considered a less severe error than assigning a high probability to a more distant stage (e.g., assigning a high probability that the patient is normotensive, in the same scenario). After each update, we calculate the RPS for each agent and each patient, then average over all patients in the test set to obtain the overall score for the given agent after the given update. Note that lower RPS values indicate better predictive performance, with 0 being the ideal score, indicating perfect predictions.

We are interested in ascertaining whether social learning enhances the predictive accuracy of the agents and, if so, which of the social learning methods introduced in section 3 proves most effective. To optimize the parameters for the social methods, we proceed as in the previous study, performing grid searches for the state-based and output-based methods and a random search for the combined method. This yields a best setting of $\alpha = 0.55$ and $\varepsilon = 0.98$ for the state-based method, of $\alpha = 0.1$ and $\varepsilon = 0.9$ for the output-based method, and of ${\alpha _1} = 0.99$ , ${\varepsilon _1} = 0.14$ , ${\alpha _2} = 0.05$ , and ${\varepsilon _2} = 0.74$ for the combined method. (See the supplementary materials for details.)

We use computer simulations to compare the social methods with optimal parameter settings both with each other and with individual updating. More specifically, we run fifty simulations, each of which starts by randomly splitting the selected NHANES data 70–30 into a training set of 410 patients and a test set of 177 patients. The 410 patients in the training set are further randomly partitioned into ten equally sized parts of forty-one patients. Each of these parts then serves as the training set of one of the interns in each of four communities of ten interns, where each community uses a different one of the four update methods in which we are interested (i.e., either individual updating or one of the three social methods).

Figure 4 shows, for the four types of communities and for each update, the average (averaged over the fifty simulations) of the average (averaged over the ten agents in the given community) RPS scored at the given update. As is already clear from the graphs, the individual updaters do, on average, worst, even by a wide margin (certainly when compared with the output-based and combined social updaters). It is equally clear that the output-based and combined social methods do better than the state-based social method. Although less clear, it seems that the output-based method does, at least for most of the updates, slightly better than the combined method.

Figure 4.

Per-update average (with 95 percent bootstrap confidence intervals) over fifty simulations of mean RPS achieved by agents, shown separately for the four communities of ten agents. (See the text for further explanation).

All of this is confirmed by the ANOVAs with post hoc $t$ -tests that we conducted for the simulation results per update. The outcomes are reported in the supplementary materials, which show, among other things, that the ANOVAs were all highly significant and that they all had ${\omega ^2}$ -values well above 0.16. (As mentioned earlier, values for this statistic above 0.14 indicate a large effect size.) These outcomes were to be expected in light of figure 4, given the notable differences between, on one hand, the nonsocial and, on the other, all of the social updating methods. The results from the Cohen’s $d$ -tests that were also part of the follow-up tests are more informative and show that choosing a social method over nonsocial updating mostly has a large ( $d \gt 0.8$ ), and always at least a medium ( $d \gt 0.5$ ), impact on accuracy. And choosing the output-based method over either of the other social methods has at least, ultimately, a medium impact on accuracy. (See, again, the supplementary materials for further details.)

As we did in the first study, we end by looking at the total accuracy the agents achieved during the training process, using again the AULC. The measure of accuracy in the second study is a scoring rule that assigns penalties to agents. So, whereas in the first study, we were interested in which updating method achieved the largest AULC, in this study, better performance is indicated by a smaller area under the learning curve. A one-way ANOVA reveals a significant and substantial effect of the updating method on the accuracy of predictions; $F\left( {3,1996} \right) = 189.73$ , $p \lt 0.0001$ , ${\omega ^2} = 0.22$ . Follow-up $t$ -tests confirm that all types of social updaters achieved significantly greater accuracy than individual updaters, which achieved a mean AULC of 11.61 ( $ \pm 5.02$ ; smallest $t = 11.71$ , all $p{\rm{s}} \lt 0.0001$ , smallest $d = 0.68$ ). Furthermore, the state-based method users, which achieved a mean AULC of 8.74 ( $ \pm 2.14$ ), did significantly worse than both the output-based method users, with a mean AULC of 7.83 ( $ \pm 1.09$ ; $t = 8.63$ , $p \lt 0.0001$ , $d = 0.96$ ), and the combined method users, with a mean AULC of 8.75 ( $ \pm 2.14$ ; $t = 6.56$ , $p \lt 0.0001$ , $d = 0.39$ ). Finally, the output-based method users did significantly better than the combined method users, though the size of the effect is small in this case ( $t = 3.28$ , $p \lt 0.005$ , $d = 0.18$ ).