Lately I’ve been asking myself: “Why are we so divided as a nation, planet, and species?” “Have things ever been this bad?” “What will become of humans in the future?” To answer some of these questions, I created a series of articles aiming to analyze, interpret, and hopefully change for the better some of the most intriguing societal issues of our time using the amazing power of behavior analysis. I know it seems pretentious to think that I can fix the world’s problems, but we are bleeping behavior analysts and we can do anything! This is the first article in this series discussing trust within society.
Don’t Trust Anyone, Just Trust Their Data:
A Behavioral Look at the Rise and Fall of Trust in Our Society
Ever since I was young, I’ve had a problem with trusting big institutions. Let me give you an example of why…
Recently, I had just gotten off a painfully long cross country plane ride at a pace that could be only be described as a slow crawl.
As I meandered with an unsteady gait into the “pickup” area, I immediately looked down at my phone, ready to call an Uber, when a sudden, awful feeling came over me. I remembered: “I can’t call an Uber at this airport.” Several words tumbled out of my mouth that are not appropriate or helpful for this article, but needless to say, they expressed my disdain for the behavior I was about to engage in: taking a taxi.
I know, I know — I’m sure words like “snob” and “spoiled” come to mind, but I don’t care: The mere thought of a taxi ride has become a conditioned aversion of its own. As I looked up, grief-stricken, my eyes begrudgingly identified an available taxi. I took a deep breath, gathered myself, and walked over to the driver. With each step, I tried to convince myself that this time would be different; this time I would have a good experience riding in a cab.
Breaking news: I didn’t. It was worse than most of my other trips. The cab smelled like an animal had recently expired in the backseat, the driver flew down the street at a speed I can only describe on the Spaceballs scale of “Ludicrous,” and when I asked for a charger for my phone, he handed me a cord that said “Nokia 6120” on it.
When I finally arrived at my condo building, my heart rate and blood pressure began to slow to pre-heart-attack levels. When I pulled out my credit card, the driver explained that his credit card machine was “out of order” and that he could only accept cash. Irritated and homesick, I handed him $80 in cash for the $79 fare. He looked at me and aggressively said “Tip” in a tone that didn’t clearly indicate whether it was a question or an order. I replied with a rebellious “No!” and began to exit the deathmobile. As my second foot broke the plane of the door, the car sped away, almost taking my leg with it. Watching him ride off into the distance on his way to risk someone else’s life, I thought to myself, “This is why I don’t trust cab drivers.” I wished I could tell the world to watch out for him.
Trust (Behaviorally Speaking)
Before delving into the thesis, it is important to touch on what “trust” is from a behavior analytical standpoint and…in my humble opinion (IMHO). Dictionary definitions center around concepts such as believability, or having confidence in someone or something. Moreover, in behavior analysis, the trustworthiness of a particular event is based on believability — namely, the accuracy, reliability, and validity of data.
This concept of trust in behavior analysis is oftentimes extended to the behavior of people. The data analysis that we do is more of a private event, as we unofficially record people’s behavior privately in order to measure the congruence between their verbal and non-verbal behavior. In other words, “Did they do what they said they were going to do?” (i.e. say-do correspondence). Repeated confirmations of these observations help to build trust between people or, in some cases, people and institutions. In essence, we are looking for a covariance between what people say they are going to do, and what they actually do. When these confirmations happen, the level of trust between entities increases, and when they do not, the level of trust decreases. Trust, in a lot of areas, is really a risk assessment, a determination of the probability of encountering a particular type of stimulus (aversive or reinforcing). In the end, trust means that our private event network can better predict the behavior of others because the person or institution that you trust has established stimulus control over that behavior.
Over the last decade or so, we have seen a dramatic shift in trust within society, specifically, who to trust and how much trust to give. This is particularly true within social media with regard to how and why we choose to offer our trust. Social media has allowed for this shift away from institutions, such as banks and big business (and taxi cabs, grrrr), that are in the process of having their hands pried off the levers of power through public censuring and the occasional auto-da-fé and into the hands of the people by way of data.
In other words, a trust shift is occurring with the reemergence of person-to-person data sharing via social media, and larger, well-established institutions are not faring well in the court of public opinion.
Strangers: No Longer a Danger?
Remember the phrase “stranger danger?” We use it often as applied behavior analysts to reference programs with which we work that teach individuals how to discriminate when interacting with other members of the community (i.e. “It’s ok to talk to this person, but not that person”). We use catchphrases as prompts for evoking safe behavior: “Never get into a car with a stranger” or “Don’t talk to strangers.” Oddly enough, these programs may be changing based on some recent advances in social media.
Consider the following social media sites: Airbnb, Tinder, Uber, Bitcoin, Facebook, LinkedIn, and Yelp. All of those platforms are social networks that allow for the establishment and maintenance of trust (by way of review data) in order to help consumers decide which behavior to engage in that will lead to reinforcement in the most efficient and effective manner.
Consider the services provided by those platforms and how we — strangers — use them to feel better about trusting other strangers with some of our most intimate behaviors:
- Where are you going to sleep? (Airbnb)
- Who are you going to sleep with? (Tinder)
- Who is going to drive you there? (Uber, stupid taxis)
- How are you going to pay for the ride? (Bitcoin)
- How will you verify that person is not a weirdo? (Facebook)
- Would you like to do business with them afterwards? (LinkedIn)
- Where will you get food afterward? (Yelp)
Social media has helped bridge the trust gap between people and strangers and has created clever ways to measure “believability.” These measures and rankings are usually based on some kind of verbal report (e.g. star systems, Likert systems, etc.). Some of my friends refer to this type of information as “soft data”; nonetheless, they are still data. Although behavior analysts scoff at the use of these glorified testimonials/soft data (which are NOT scientific), take a look outside during lunchtime the next time you go to a behavior analysis conference. You may find that everyone is checking Yelp for where to get food.
Out With the “Old”
Our behavior as of late has been conditioned to “trust” these social media measures more than other sources. Those “other sources” are the institutions that we have lost faith in. Take your pick of establishments that have done us wrong over the years, from slimy banks to shady businesses (like that bleeping cab company) all the way to dirty hotels and even dirtier political parties! All of these institutions represent the outgoing administration. These institutions have very specific characteristics. They are all top-down hierarchies with centralized and secretive decision-making processes. That’s not exactly a winning formula for operating a business in 2017 — you know why? Because we want institutions that allow us to keep them in check with data, and right now the best data we can get is from person-to-person reviews (soft data).
Think about my experience with the cab ride. I would imagine that the entire journey would have been a lot more pleasant if I could have reviewed the driver’s behavior afterward on a very public platform for others to digest before deciding to ride with him — like Uber and Lyft do. The message here is that social contingencies matter a lot more now.
In with the “New”
As with many things, there is an “out with the old and in with the new” perspective. The “new” in this case is person-to-person platforms where people rely on data from other people who have used those services in the past. With people (instead of institutions) driving the conversations, the characteristics of these platforms are very different: bottom-up hierarchies, free-flowing information, and de-centralization. In other words, decision-making is transparent and open to the world, which is a much wiser course of action for building and sustaining an organization in the 21st century.
Consider the contingency change with trust. Our trust in those shady institutions has been so soiled that we are willing to trust an Uber driver who has experienced significantly less vetting for past criminal behavior than a taxi driver with a much more in-depth background check (Eveleth, 2015) simply because that driver has a good star ranking on Uber’s app.
Over the course of the past several years, we have also seen a dramatic shift in trust in politics. I don’t know if we have ever truly trusted the behavior of politicians; however, recent years have shown us that the “political establishment” of leaders from both major political parties are no longer en vogue. “Outsiders” are the “in” thing now. In other words, “institutional” brands are out, and “person-to-person” brands are in.
Pairing Is Involved
What does all of this mean in terms of behavior analytics? It means (IMHO) that conventional institutions have lost their status as conditioned reinforcers (through bad branding) and have transferred, willingly or not, that control over to person-to-person environments (which may also be institutions). Branding involves pairing. Older institutions, for example, recently have paired themselves with corruption, losses of consumer money, and overall scandal. Person-to-person platforms have paired themselves with good food, solid places to sleep, and people you want to interact with.
In the End
Through social media, we have found better ways to measure trust than we have in the past. These platforms very effectively use principles of behavior analysis. Not perfectly, but very competently. We (consumers) weren’t collecting data at all before with institutions; we were just blindly trusting them because they had clever commercials that, again, very effectively used behavior analysis (pairing) to convince us to buy their product or service. Now, everyone in almost every walk of life is held accountable to reviews and rankings on various platforms and sites using this type of soft data.
Again, this soft data tends to be in testimonial format (boooo), but the social contingencies involved with receiving stars tend to be more powerful than previous contingencies — or the lack thereof.
What Does This Mean for Behavior Analysts?
This means we need a way to build trust within the behavioral analysis community for our products, services, and our science as a whole: a platform where people can go to check us out for believability and congruence between what we say and what we do. Some of those platforms have already been created:
- The Behavior Analyst Certification Board (BACB) communicates to the world who has passed a standardized test and met requirements that a board of our most trusted leaders have agreed upon.
- The Behavioral Health Center of Excellence (BHCOE),identifies and recognizes quality ABA providers.
- The Code of Ethics for Behavioral Organizations (COEBO) identifies and credentials ethically behaving ABA companies.
- com combines the efforts of all of the other platforms into one data-driven site. BehaviorMatch.com uses data (and not testimonials) to match consumers with professionals, companies recruiting with professionals, and people just looking for training in ABA.
So if you want to make sure that we as a field are trusted, do what you say you are going to do and make sure people see your data — soft or otherwise.
(n.d.). ABOUT THE BACB. Retrieved March 19, 2017, from https://bacb.com/
Baer R.A., Detrich, R., & Weninger, J.M. (1988). On the functional role of the verbalization in correspondence training procedures. Journal of Applied Behavior Analysis, 21, 345–356.
Behavioral Health Center of Excellence. (n.d.). Retrieved March 19, 2017, from http://www.bhcoe.org/
BehaviorMatch. (n.d.). Retrieved March 19, 2017, from https://behaviormatch.com/
COEBO, Inc. (n.d.). Retrieved March 19, 2017, from http://www.coebo.com/
Eveleth, A. L. (2015, March 03). Are taxis safer than Uber? Retrieved from https://www.theatlantic.com/technology/archive/2015/03/are-taxis-safer-than-uber/386207/
Luciano, M. C., Herruzo, J., & Barnes-Holmes, D. (2001). Generalization of say do correspondence. Generalization Of Say-Do Correspondence, 2001(51). Retrieved from http://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=1318&context=tpr