One of the most important things we do on a day-to-day basis is make predictions about the value of individuals or companies, or really, any entity. Making such predictions is challenging because we have limited information about the qualities of the entity we are attempting to make predictions about. For instance:
- A hiring manager at a firm is trying to make a prediction about whether a certain applicant will be a high performer.
- A PhD admissions committee makes predictions about whether an applicant to their program will turn into a star researcher.
- A venture capitalist makes predictions about whether a startup or founding team will create a breakthrough product that will become a billion dollar company.
- A search engine is making a prediction about whether a certain webpage contains useful information for its users.
- A consumer makes predictions about the quality of a product before he/she buys it.
Predictions of this type are commonplace and often rather difficult to make. This difficulty exists for two reasons. First, only a limited set of characteristics are observable to the decision maker, whereas much else is unobservable. A hiring manger, for instance, may observe a resume and a list of references. Based on this resume and reference list, she attempts to make an inference about many things: how hard working the applicant is, their base of knowledge, their ability to get along with other members of her team, and so on. Thus, the hiring manager attempts to use “observables” to infer something about the unobservables.
The goal therefore is to map observables (the things that you can easily measure and observe about someone or some organization) to unobservables. What are some examples of unobservables and/or things that are difficult to observe:
- Whether a person you hire will “fit” with an organization’s culture
- Whether a company you invest in will turn a profit
The inability to effectively communicate information about these hard to quantify traits from one person to another becomes a problem for both the evaluator and in many cases for the person being evaluated, particularly if they are high quality, but others can’t tell this is the case.
That is, how does one separate the signal from the noise?
One solution proposed to this problem is signaling theory. People send signals and these signals contain information that allow “buyers” to ascertain whether the seller (a job market candidate) is of high quality or not. But anyone can send signals, and sometimes the signals are noisy or uninformative. If the signals are no good, then they don’t solve the asymmetric information problem.
Michael Spence argued that some signals are harder to acquire than others, and this difficulty in acquiring the signal is related to some dimension of underlying quality.
For instance, a hiring manager might be looking to hire someone with great machine learning talent. Anyone can put “machine learning” on his/her resume, so merely doing so isn’t likely to be a very good signal of having that skill. However, it is probably easier to win a Kaggle competition if you have good machine learning skills than if you do not. As a result, those with more machine learning skill are more likely to be represented among Kaggle winners than those without that skill. Thus, winning in Kaggle is likely to be a decent signal of ML skill. Further, since winning in Kaggle is easily observable, it is perhaps a decent signal for what we care about.
Can you think of other signals that contain a lot of information and are difficult to fake?
Joel Podolny in a series of articles proposed that social relations also help signal quality. This is a profound idea, and I will walk through it further. But let us fast forward to another application of Eigenvector centrality: the original Google PageRank algorithm.
For example, social cues such as endorsements, recommendations, funding decisions or hiring decisions, convey/signal information.
Consider James and Betty. Both have two connections of their own. And both of their connections think highly of them and recommend them. In an abstract sense, Betty and James are rated by their raters-e.g., their two connections. But a new problem arises: who has more reliable raters?
This is what we can consider the “rating the raters” problem. While in the first degree out (the direct connections of these two individuals) they are indistinguishable, there is substantial variation in their second and third degree ties. Although James and Better have similarly sized networks, Betty’s network connections have far more connections of their own.
While it is relatively easy to figure out the difference between the size of Betty and James’ second degree network, the problem gets more complicated the further we move out. Real networks don’t usually have connections out to the 2nd or 3rd degree, but to 4th, 5th, 6th, etc. The second problem is that real networks aren’t usually trees. Networks loop back on themselves over and over again which make the “rating the rater” problem hard. So we cannot just re-weight the rating by the ratings received by the rater.
There is concept, called Eigenvector centrality, that does exactly what we thought was hard: it rates the raters, the rater’s raters, the rater’s rater’s raters, and so on. This measure gives us a nice summary statistic telling us how much “status” a node in the network has. Hard to fake because you can perhaps fake your own network ties, but not the ties of your connections’ connections. The nodes below, for instance, are resized by eigenvector centrality.
The problem of determining the “value” or credibility of an object based on its connections and its connections’ connections is a general one. Google’s original algorithm, PageRank, is sociometric status. The basic intuition of PageRank was if a site gets a lot of incoming links, and the sites linking to the original site also do, and so on. Then there must be some value to it. The insight arises by viewing the Web as a network, and using its structure to determine whether a page is useful or not.
Ego and Altercentric Perspectives
Now that we have the basic concept of sociometric status down. The “big idea” in sociology came from Joel Podolny. He suggested that we had focused primarily on seeing networks as “pipes” through which information, resources, support, and other “stuff” flows. However, networks are also useful for individuals in resolving problems of uncertainty because certain types of network structures also signal trust, reputation, and identity — network structures are prisms that reveal information as well.
The extent to which networks operate as pipes or as prisms depends on the level of uncertainty faced by market participants. He developed a highly useful framework for thinking about characterizing what structure may matter when. There are two types of uncertainty, Egocentric and Altercentric.
Fig. 1.—Illustrative markets arrayed by altercentric and egocentric uncertainty
A market or market segment can rate highly on one type of uncertainty without rating highly on the other.
Consider the four markets represented in the figure above. From Podolny (2001):
Vaccines: Beginning with the market for a particular vaccine, such as polio or smallpox, in the upper left-hand quadrant. The most salient source of uncertainty in this market is that which underlies the development of the vaccine. Once the vaccine is developed and is given regulatory approval, there is little uncertainty on the part of consumers as to whether they will benefit from the innovation. Accordingly, a market for a vaccine is a market that rates high on egocentric uncertainty, but low on altercentric uncertainty.
Roofers: Alternatively, consider the market in the lower right-hand corner, a regional market for roofers. “Roofing technology” is relatively well understood, and while roofers may face some uncertainty as to who needs a roof in any particular year, they can be confident that every homeowner will need repair work or a replacement every 20 years or so. By sending out fliers or advertising in the yellow pages, they can be assured of reaching a constituency with a demand for their service. However, because an individual consumer only infrequently enters the market, the consumer is generally unaware of quality-based distinctions among roofers. The consumer may be able to alleviate some of this uncertainty through consultation with others who have recently had roof repairs; however, the need for such consultation is an illustration of the basic point. Only through such search and consultation can the consumer’s relatively high level of uncertainty be reduced. Accordingly, this is a market that is comparatively low in terms of egocentric uncertainty, but relatively high in terms of altercentric uncertainty.
What are some other examples of markets that are low on one type of uncertainty and high on another? What about markets that are high on both?
How does one deal with altercentric uncertainty?
Let us loop back to our earlier discussion of sociometric status. Why is sociometric status a useful signal to help resolve altercentric uncertainty?
- Sociometric Status: A position in a social network – defined by the ties that you have to others – where you receive deference from others who are themselves highly respected or deferred to.
When does Status goes awry?
However, there are many instances where status does not serve as a perfect signal of quality – and this can lead to mis-perceptions of status and thus misperceptions of quality. When status is a perfect signal of quality it is said that there is tight coupling between status and quality. However, as a I mentioned, this is often not the case.
Matthew Effect / Self-fulfilling prophecy: The classic example of this is the phenomenon of the 41st chair. This is the example of the “French Academy” where there are only 40 chairs, and there perhaps no substantive difference between #40 and #41 – but the 40th person becomes a holder of a chair, and the 41st person does not. This results in the 40th person get more rewards, recognition, etc. Which in turn allows them to do better work – because they now have significantly more resources than people who do not. In sociological parlance, the phenomenon of the 41st chair is called “Decoupling.” Here, the linear relationship between quality and status – the 40th person gains far more status than the 41st—breaks down.
Buy low, sell high: This decoupling is an arbitrage situation for managers – because most people use status signals that are imperfect. There are two possible strategies to exploit this gap:
- Figure out a more readily observable representation of social signals that maps onto to quality more tightly and sell that information.
- Figure out a way to measure sociometric status in a situation where it is not currently used. Then use this as a better way of valuation.
Beyond the basics
The study of sociometric (and other Status) is an extremely rich area of research in organizational sociology and economic sociology. I have merely scratched the surface of this topic.
Some excellent articles and reviews in this stream include:
Stuart, Toby E., Ha Hoang, and Ralph C. Hybels. “Interorganizational endorsements and the performance of entrepreneurial ventures.” Administrative science quarterly 44.2 (1999): 315-349.
Sauder, Michael, Freda Lynn, and Joel M. Podolny. “Status: Insights from organizational sociology.” Annual Review of Sociology 38 (2012): 267-283.
Lynn, Freda B., Joel M. Podolny, and Lin Tao. “A Sociological (De) Construction of the Relationship between Status and Quality.” American Journal of Sociology 115.3 (2009): 755-804.
Chen, Ya-Ru, et al. “Introduction to the special issue: Bringing status to the table—attaining, maintaining, and experiencing status in organizations and markets.” (2012): 299-307.
Phillips, Damon J., and Ezra W. Zuckerman. “Middle-Status Conformity: Theoretical Restatement and Empirical Demonstration in Two Markets.” American Journal of Sociology 107.2 (2001): 379-429.