Stay Home or Return to Campus? College Students’ 2020 Dilemma & Diffusion
Over the summer this year, as fall semester was quickly approaching, returning college students were faced with the inevitable, weighty question: Should I stay at home or return to campus? David Rosowosky in “Should I Stay Or Should I Go: Colleges’ And College Students’ Next Dilemma” retrospectively analyzes these very concerns, pointing out that many students’ main motivations for returning to campus were so that they could live independently and spend time with friends. After all, the “college experience” is never truly complete without these incredibly important relationships. Inevitably, though, he saw that many students still chose to stay at home, a decision, he noted, made on their own or from their parents’ urging and advice.
Already, we can see that a student’s choice to return to campus or stay at home depended on their social network, with influence emanating from the student’s parents or family. Surprisingly, Rosowosky consistently fails to note the role of peer influence. From my personal experience, a college student’s decision to stay at home or return to campus depended largely on whether or not their close friends would return to campus or not. At least this was what occurred in my own social network. Thus, in terms of networks, Rosowosky fails to grasp the importance of local network diffusion among close friends in a student’s social network in their decision to stay at home or return to campus. Because Rosowosky fails to grasp this critical point, I decided to analyze a simplified version of my own network of close friends to demonstrate how diffusion can cause college students to switch from a decision of returning to campus, to staying at home for the Fall 2020 semester, which is what occurred in my network. I incorporate strong and weak ties to reflect the strength of relationships between each friend and sketch a possible sequence of events that, through diffusion, led to a cascade of deciding to stay at home. Diffusion applies well to this scenario because we are mostly concerned about how changes spread locally to close friends, or our local network, and not as much with the general population, such as the general student population at university.
In my social network diagrams, bolded lines represent a strong tie, which occurs if two friends are very close, while thinner lines represent a weak tie. For simplicity’s sake, let us assume that the primary reason for going back on campus is so that the student can spend time with their friends while studying. We will ignore the variable personal factors such as a strained relationship with parents/family, leases for off-campus housing, etc. Thus, it makes sense to say that if you return to campus but most of your friends stay home, then it is not worth going on campus because you will be left feeling isolated. If most of your friends do go back to campus, then it is worth going back to campus.
Initially, all nodes in the social network decide to return to campus, except for some “initial adopters” who now decide to stay at home because of distance from campus. For example, for international students or for students who must drive 10+ hours or take a plane, returning to campus is a huge hassle, especially in the case that campus decides to close down and send everyone home.
Making the same decision to stay at home or return to campus with a friend/acquaintance benefits both you and your friend. Let us assume that sharing the same decision of returning to campus along a weak tie has a benefit of 3, while sharing the decision of returning to campus along a strong tie has a benefit of 6. Sharing the same decision of staying home along a weak tie has a benefit of 4, while sharing the decision of staying home along a strong tie has a benefit of 8. If two friends make opposite decisions (i.e. one friend decides to return to campus but the other stays home), then they both receive a benefit of 0.
The benefit of both friends staying home is slightly greater than the benefit of returning to campus because at that point of the summer, COVID-19 cases were still climbing. Things were not looking like they were going to get better, so most of us were uncertain about how university and other students were going to handle the situation. Additionally, the added mental stress of studying and going about everyday life with COVID-19 (such as wearing masks, social distancing, sanitizing everything, etc.) can be eliminated simply by staying at home. However, each of us would still want to return to campus unless there were not enough friends going.
The diffusion that occurs in this network is: switching from returning to campus —> staying home.
NODE M
The payoff of Node M for choosing CAMPUS =
benefit of M-Me both choosing CAMPUS in a strong tie
+ benefit of M-A making different decisions (CAMPUS VS. HOME)
+ benefit of M-J making different decisions
+ benefit of M-R both choosing CAMPUS in a weak tie
+ benefit of M-Y making different decisions
= 6+0+0+3+0 = 9.
The payoff of Node M for choosing HOME =
benefit of M-Me making different decisions
+ benefit of M-A both choosing HOME in a strong tie
+ benefit of M-J both choosing HOME in a strong tie
+ benefit of M-R making different decisions
+ benefit of M-Y both choosing HOME in a weak tie
= 0+8+8+0+4 = 20.
Because the payoff of M for choosing HOME > payoff for choosing CAMPUS (20 > 9), M will switch to staying at home.
NODE R
Now that M has switched to staying at home, we turn to analyzing R using the same kind of calculations. Thus, we get:
The payoff of Node R for choosing CAMPUS = 0+6+0+0+0+6 = 12.
The payoff of Node R for choosing HOME = 8+0+4+4+4+0 = 20.
Because the payoff of R for choosing HOME > payoff for choosing CAMPUS (20 > 12), R will switch to staying at home.
NODE B
Now that R has switched to staying at home, we turn to analyzing B using the same kind of calculations. Thus, we get:
The payoff of Node B for choosing CAMPUS = 0.
The payoff of Node B for choosing HOME = 8.
Because the payoff of B for choosing HOME > payoff for choosing CAMPUS (8 > 0), B will switch to staying at home.
NODE Me
Now that B has switched to staying at home, we turn to analyzing Me using the same kind of calculations. Thus, we get:
The payoff of Node Me for choosing CAMPUS = 0+0+0+0+0.
The payoff of Node Me for choosing HOME = 8+8+8+8+8 = 40.
Because the payoff of me for choosing HOME > payoff for choosing CAMPUS (40 > 0), I will switch to staying at home.
This diffusion scenario is in many ways a coordination game, where each person may coordinate with their friends and benefit from friends that make the same decision, much like when two people benefit when they use the same technology, which can eventually lead to a threshold-based diffusion. This spread of behavior is also more similar to the spread of innovation/technology than the spread of information, as discussed in lecture. Information often comes from weak ties like acquaintances, while technology/innovation is fueled more by the availability of strong ties, which took up the majority of my social network.