Simulating macro-level effects from micro-level observations*
Edward Bishop Smith
Northwestern University
Bill Rand
University of Maryland
June 2014
Word Count:
*
I have benefited from the feedback of participants at the 2014 Social Hierarchy Conference at the Arison School of Business,
IDC Herzliya, Israel. Correspondence to Ned Smith, Kellogg School of Management, Northwestern University, 2001 Sheridan
Rd, #358, Evanston IL, 48019, [email protected].
Introduction
In this proposal memo I argue, mostly by way of an example, that social science
researchers who conduct their research with human subjects in controlled laboratory settings
should consider using computer-based simulations to analyze the "macro" implications of their
"micro" findings. While bringing these two modes of research together involves overcoming
both social and technological barriers, I propose that the barriers represent minor impediments in
comparison to the value that integration could create. The value of bringing together empirical
lab research with computational and simulation modeling is likely to come in three forms, at
least. First, simulation research will benefit from exposure to a larger set of mechanisms and
observed behaviors that can add significant nuance and refinement to existing simulated models
of human behavior and collective outcomes. Second, simulation offers empirical researchers a
way to assess the feasibility and potential importance, as opposed to merely the statistical
significance, of findings. For researchers in social psychology in particular, simulation offers an
opportunity to address questions about the external validity of both research methods (e.g.,
priming) and findings. Finally, simulation provides a method for identifying long-run
implications of what are typically single period observations of human behavior in a lab.
In this brief memo I offer an example of what it looks like to move research findings
from the behavioral lab to the computer lab. In contrast to "top down" modeling approaches
where researchers begin with an "already emerged collective phenomena [and] seek microrules
that can generate them" (Epstein and Axtell, p. 20), the example presented here represents a
"bottom up" approach to simulation modeling. Specifically, I use the results of existing
experimental research conducted in a behavioral lab to construct a world whereby individual
agents are programmed to behave in a manner consistent with the laboratory findings. The result
is the simulated projection of a question that I propose all behavioral researchers should ask—
though often do not—each time they report a new finding; "what would the world look like if
people actually behaved in the ways they just did in my laboratory?"
Threat, Status, and the Cognitive Activation of Social Networks
In 2012 I published a paper along with two coauthors that, at the time, led me into
unfamiliar methodological territory, the human subjects lab (Smith, Menon, and Thompson
2012). The origin of the project was straightforward: If people turn naturally to friends and close
confidants when they feel threatened and if people are more likely to find employment via
structurally "weak" social network ties (Granovetter 1974), then what happens when the threat
one encounters is job loss? Do people trim their weak network ties at the very moment when
those ties are the most important? Moreover, do people different systematically in the way they
conceptualize their networks—"cognitively activate" is the term on which my coauthors and I
settled—following the onset of job threat? Variation in the way people think about their
networks following the loss of a job, we reasoned, should carry over to have an impact on how
people network in the active sense and ultimately impact an individual's success in finding
subsequent employment.
We first used data from the General Social Survey (GSS) to turn our largely conceptual
question into an empirical one. The data revealed a significant association among the size of
respondents' social networks, their self-reported socioeconomic status, and their responses to the
question, "How likely is it that you will lose your job in the next 12 months?" Specifically, we
found that respondents who self-identified as having low socioeconomic status and perceived a
moderate to significant threat of job loss reported having smaller networks than like-status
respondents who felt no such threat. Among high-status respondents, by contrast, moderate to
significant job threat was associated with larger networks. Unable to address concerns about
reverse causality with a cross-sectional database, we opted to take our research into the lab. Here
we measured participants' perceptions of their social status, randomly assigned them to
conditions of either high or low job-related threat, and then asked them to recall their social
network contacts. Our findings were substantively identical to findings from the GSS; low-status
people reported having smaller networks when under threat then when not under threat. Highstatus people reported larger networks when under threat then when not under threat.
In addition to the psychological implications of our findings, a discussion of which I will
table here for the sake of brevity, our results presented a handful of practical implications as
well. For example, we were (and continue to be) interested in identifying interventions that could
prevent low-status people from shifting their attention to the core of their social networks
following the onset of job loss (e.g., Menon and Smith 2014). We were also (and, again, continue
to be) interested in knowing whether our cognition-level findings could help explain variance in
labor market outcomes between low- and high-status people, particularly, though not only,
during periods of economic turmoil when the frequency of layoffs increases. Building on this, as
the "macro"-oriented member of the research team, I was fascinated in knowing what
communities and economies might look like if everyone behaved in reality in the way that our
laboratory participants had behaved in a controlled, if not contrived situation.
Simulating Macro-Patterns from Micro-Observations
To identify in the real world the kinds of behaviors my coauthors and I uncovered in the
laboratory would be extremely complex. To start, one would need to (1) reliably measure
individuals' full social networks—what my coauthors and I call people's "potential networks"—
so as to know to whom the individual could turn following the onset of job threat or job loss, and
(2) gather a sufficiently large sample of individuals to ensure that a reasonable number of those
individuals would at some point lose their jobs and respond to that loss in sufficiently varied, yet
measurable ways. While online social media sites such as LinkedIn render these two objectives
more feasible now than compared to 15 years ago, the availability of big, electronic datasets are
in no way a cure-all. As Simon (1987) pointed out, it is simply very hard, and will likely always
be very hard, to use real-world empirical data to analyze the link between psychology and
individual behavior and macro, society-level outcomes.
This difficulty does not mean, however, that social science researchers should narrow
their focus to a single level or even immediately adjacent levels of analysis. Economists, notably,
have long analyzed how individual behavior and psychological orientation—most notably,
rationality—can aggregate to explain macro-level phenomenon by way of formal, closed-end
mathematical modeling and by solving for steady state, long-run equilibriums. Simulation and
agent based modeling (ABM), too, offers tools by which to examine the effects of individual
behaviors on macroscopic patterns, though unlike the closed-end models used by economists,
ABMs need not converge to any equilibrium state and typically allow for significantly more
heterogeneity among individual "agents." While my goal here is not to summarize ABM—see
Epstein and Axtell (1996) for an introduction to the topic and an excellent comparison between
simulation and mathematical modeling—suffice it to say that the sheer heterogeneity of people,
cognitions, and behaviors that have been identified over decades of experimental research is
better suited to the ABM approach.
The Effects of Network Winnowing and Widening on the Distribution of Wealth
What would happen if low-status people systematically narrowed and high-status people
systematically widened their social networks following job loss? How would the network
structure of a community evolve? What would happen to the overall distribution of wealth in
society? Finally, would the long-run macroscopic outcomes of these individual network
winnowing and widening behaviors produce anything that resembles the real world? To address
these questions we can simulate a world of agents and program those agents to behave according
to rules that we define (which I am arguing we should base on findings from human subject
labs).
To begin, let's give each person in our simulated world a level of wealth, Wi, and an
earning rate, Ei, drawn from a normal distribution. Wi increases over time as a function of Ei, or,
Wi = Wi,t-1 + [Ei,t-1 * Wi,t-1]. Next, let's connect the people in our world in such a way that they
form what is known as a "preferential attachment" network similar to the one shown in Figure 1.
As we are interested in the effects of different networking behaviors following the onset of job
loss, we next need to define rules by which people loss their jobs. To start, let's say that ten
percent of the people in our world lose their jobs each year, at random. When people lose their
jobs, their earning rates become zero. To re-enter the labor market, and reestablish an earning
rate other than zero, people look to the social networks, but they do so in different ways.
Following the findings I described above, let's say that those with existing wealth above the
median wealth of all individuals in our world—i.e., high status people—respond to job loss by
making a new network connection with, and subsequently taking on the earning rate of (i.e., they
get hired by) the wealthiest friend of one of their current friends (see figure 2, left side). By
comparison, low-status people respond to job loss by cutting the weakest tie from their existing
networks and taking on the maximum earning rate of those left (see figure 2, right side).
The first three panels in figure 3 show the starting, short-run, and long-run states of our
simulated world using screen output from the agent based modeling software, NetLogo. The
fourth, bottom-right panel shows the long-run outcome of our model when increasing numbers of
people follow the low-status approach overtime. What does the output reveal? First, differences
in networking responses to job loss have a dramatic effect on the overall connectedness of the
community. Second, the initial normal distribution of wealth ultimately transitions into a bimodal
distribution of rich and poor over the long run. Third, the short-run effect of agent's networking
behavior has a positive effect on the median amount of wealth and shifts the entire wealth
distribution to the right. Even low-status people tend to benefit from networking when their
networks are still sufficiently broad. We might even say that as a result of early networking, an
upper-middle class begins to take shape in the short-run (see figure 3, northeast panel). Over the
long-run, however, the networking, and thus earning opportunities of high- versus low-status
people diverge and the once growing upper-middle class begins a swift breakdown into the
bimodal distribution that characterizes the low-run steady state.
Extending the Model: Altering Parameters and Building New Theory
It is likely that any reader will by this point have already thought of a handful of changes
to the model just presented. For instance, what happens as the number of agents increases or the
beginning distribution of links is altered? One might also want to alter the unemployment rate or
assign unique probabilities of becoming unemployed to individual agents, perhaps a function of
their current earning rate, their accumulated wealth, or their network of connections. Each of
these changes amounts to an experiment that can enable a researcher to analyze the importance
of individual parameters in affecting macroscopic outcomes. Importantly, as the number of
parameters increase researchers can begin to pinpoint which parameters are central to emerging
trends and which result in only trivial differences, or alternatively, create patterns that are
illogical and contrary to existing empirical observations. While such experiments are often
impossible to run in the behavioral lab or real world, they constitute little more than a coding
challenge for simulation research.
We might also consider more complex versions of our model that intend not to test the
feasibility and validity of laboratory findings, but help construct new theory by which to explain
the origins of certain behaviors. For example, thus far our model presupposes that high- and lowstatus people follow fixed yet contrasting rules of behavior following the event of job loss. While
this may in fact be true, it tells us little about how these behavioral trends came to be. Here, too,
simulation can be useful. Imagine that instead of following pre-determined rules, all actors,
regardless of their wealth, react to job loss the way high-status actors do in the description above,
scanning the networks of their friends and attempting to form a new tie to a wealthy friend-of-afriend. Now, instead of assuming that all tie formation attempts are successful, we might opt to
calculate a probability that the target accepts a job seeker's invitation to form a tie as a function
of the seeker's wealth or perhaps the wealth gap between the seeker and target. In making this
adjustment, we set the stage for agent learning; for example, we may dictate that the more often a
seeker is rejected by a target, the more likely the seeker is to turn towards "strong" network ties
in the future.
Conclusions
I recognize that I've already used up more than my allotted space in preparing this memo
and will only briefly use this "conclusions" section to say that I would welcome the opportunity
to develop this note into a full-length manuscript for the ORM special issue. In addition to
further developing the model I've presented, a full-length manuscript would also provide a short,
formal introduction to agent-based modeling and possible (if space permits) consider a second,
organization-specific simulation model. I would also like an opportunity to discuss how
simulation modeling may be particularly useful for social psychology in the face of the current
"replication crisis." I look forward to hearing back from you.
References
Epstein, J. and R. Axtell. 1996. Growing Artificial Societies: Social Science From the Bottom
Up. Brookings Institution Press / MIT Press. Cambridge, MA.
Granovetter, M. 1974. Getting a Job: A Study of Contacts and Careers. Harvard University
Press, Cambridge, MA.
Menon, T. and E.B. Smith. 2014. "Identities in flux: Cognitive network activation in times of
change." Social Science Research, 45: 117-130.
Simon, H. 1987 "Giving the soft sciences a hard shell." Boston Globe (3 May).
Smith, E. B., T. Menon, and L. Thompson. 2012. "Status Differences in the Cognitive Activation
of Social Networks." Organization Science 23(1).
Figure 1: Preferential Attachment Network. The distribution of links in a preferential attachment
follows a power-law distribution, meaning that few people have a lot of links and most people
have a just a few links.
Figure 2: Network Winnowing and Network Widening. High-status people (left) create a new tie
with the wealthiest friend-of-a-friend, here E, and take on the earning rate of that person. Lowstatus people (right) cut the "weakest" tie from their existing network and take on the maximum
earning rate among those remaining in their networks.
E
A
B
C
HS
D
A
B
C
LS
D
Figure 3: Simulation Results, NetLogo Screenshots. Upper-left: Setup. Upper-right: Short-run
outcome. Lower-left: Long-run outcome. Lower-right: Long-run outcome when smaller
percentage of people are defined as high-status. The buttons in the upper-left corner of each of
the screen shots setup and run the simulation. The three sliders control the number of agents, the
beginning distribution of network links, and the unemployment rate. The top histogram shows
the distribution of wealth. The bottom histogram shows the distribution of links. The larger
window shows the network of agents. Agents are color according to their wealth.