Archive for the ‘Organizations’ Category
Complex decision making as an infinite game
A decision is the act of choosing between two or more options.
There are two kinds of decisions, computable and non-computable [1]. In the former, options are well-defined and finite in number, and there are unambiguous facts (data) available based on which options can be rated. In the latter, options are neither clear nor enumerable and facts, if available at all, are ambiguous.
Computable decisions are simple, non-computable decisions are complex. We’ll refer to the two decision types by these names in the remainder of this article.
An example of a simple decision is buying a product (TV, car or whatever) based on well-defined criteria (price, features etc.). An example of a complex decision is formulating a business strategy.
It should be clear that simple decisions involve smaller temporal and monetary stakes – i.e. the cost of getting things wrong is limited and the effects of a bad decision wear off in (a relatively short) time. Neither is true for complex decisions: the cost of a poor choice can be significant, and its negative effects tend to persist over time.
A key feature of complex decisions is that they (usually) affect multiple parties. That is, they are socially complex. This has implications regarding how such decisions should be approached. More on this later.
Conventional decision theory is based on the notion of maximizing benefit or utility. For simple decisions it is assumed that utility of each option can be computed; for complex decisions it is assumed they can be estimated, or at least ranked. The latter assumption is questionable because each party affected by a complex decision will have its own notion of utility, at least at the outset. Moreover, since neither options nor facts are unambiguous at the start, it makes little sense to attempt to estimate utility upfront.
The above being the case, it is clear that complex decisions cannot be made on the basis of maximizing utility alone. Something else is needed.
–x–
James Carse’s classic book, Finite and Infinite Games, begins with the following lines:
There are at least two kinds of games. One could be called finite, the other infinite. A finite game is played for the purpose of winning, an infinite for the purpose of continuing the play.
A finite game ends when a player or team wins. However, “just as it is essential for a finite game to have a definitive ending, it must also have a precise beginning. Therefore, we can speak of finite games as having temporal boundaries.”
The parallel between simple decisions and finite games should be evident. Although less obvious, it is useful to think of a complex decision as an infinite game.
When making a complex decision – such as a business strategy – decision-makers will often focus on maximising potential benefits (aka utility). However, as often as not, the outcome of the decision will fall far short of the expected benefits and may, in some cases, even lead to ruin. This being so, it is perhaps more fruitful to focus on staying in the game (keep playing) rather than winning (maximising utility).
The aim of a complex decision should be to stay in the game rather than win.
How does one ensure that one stays in the game? Heinz von Foerster’s ethical imperative offers an answer”
Always act to increase your choices.
That is, one should decide in such a way that increases one’s options in the future thereby increasing chances of staying in the game. One can frame this discussion in terms of adaptability: the greater the number of options the greater the ability to adapt to unexpected changes in the environment.
How can one “act to increase one’s choices”?
One way to do this is to leverage social complexity: get different parties to articulate their preferred options. Some of these options are likely to contradict each other. Nevertheless, there are ways to handle such a diversity of potentially contradictory views in an inclusive manner (for an example, see this paper; for more, check out this book). Such an approach also ensures that the problem and solution spaces are explored more exhaustively than if only a limited number of viewpoints are considered.
The point is this: there are always more options available than apparent. Indeed, the number of unexplored options at any stage is potentially infinite. The job of the infinite player (decision-maker) is to act so as surface them gradually, and thus stay in the game.
–x–
Traditionally, decision-making is seen as a logical undertaking based on facts or data. In contrast, when viewed as an infinite game, complex decision-making becomes a matter of ethics rather than logic.
Why ethics?
The answer lies in von Foerster’s dictum to increase one’s choices. By doing so, one increases the chances that fewer stakeholders’ interests are overlooked in the decision-making process.
As Wittgenstein famously said, “It is clear ethics cannot be articulated.” All those tedious classes and books on business ethics miss the point entirely. Ethical matters are necessarily oblique: the decision-maker who decides in a way that increases (future) choices, will be acting ethically without drawing attention to it, or even being consciously aware of it.
–x–
Any honest discussion of complex decision-making in organisations must address the issue of power.
Carse asserts that players (i.e. decision-makers in the context of this article) become powerful by acquiring titles (e.g. CEO, Manager etc.). However, such titles can only be acquired by winning a finite game– i.e. by being successful in competitions for roles. Power therefore relates to finite rather than infinite games.
As he notes in his book:
Power is a concept that belongs only in finite play. To speak meaningfully of a person’s power is to speak of what that person has already achieved, the titles they have already won.
Be that as it may, one cannot overlook the reality that those in powerful positions can (and often do) subvert the decision-making process by obstructing open and honest discussion of contentious issues. Sometimes they do so by their mere presence in the room.
How does a complex decision-maker deal with the issue of power?
Carse offers the following answer:
How do infinite players contend with power? Since the outcome of infinite play is endlessly open, there is no way of looking back to make an assessment of the power or weakness of earlier play. Infinite players look forward, not to a victory but toward ongoing play. A finite player plays to be powerful; the infinite player plays with strength. Power is concerned (and a consequence of) what has happened, strength with what has yet to happen. Power will be always restricted to a relatively small number of people. Anyone can be strong.
What strength means is context-dependent, but the following may help clarify its relationship to power:
Late last year I attended an end-of-year event at the university I teach at. There I bumped into a student I had mentored some time ago. We got talking about his workplace (a large government agency).
At one point he asked, “We really need to radically change the way we think about and work with data, but I’m not a manager and have no authority to initiate changes that need to be made.”
“Why don’t you demonstrate what you are capable of? Since you are familiar your data, it should be easy enough to frame and tackle a small yet meaningful data science problem.” I replied.
“What if my manager doesn’t like my taking the initiative?”
“It is easier to beg forgiveness than seek permission.”
“He might feel threatened and make life difficult for me.”
“If management doesn’t like you’re doing, it’s their loss. What’s the worst that could happen? You could lose your job. With what you are learning at university you should have no trouble moving on to another role. Indeed, by doing so, you will diversify your experience and increase your future options.”
–x–
To summarise: when deciding on complex matters, act in a way that maximises possibility rather than utility. Such an approach is inherently ethical and enhances one’s chances of staying in the game.
Complex decision making is an infinite game.
[1] There are many other terms for this classification: tame and wicked (Horst Rittel), programmed and non-programmed (Herbert Simon), complicated and complex (David Snowden). Paul Culmsee and I have, perhaps confusingly, used the terms uncertain and ambiguous to refer to these in our books. There are minor contextual differences between how these different authors interpret these terms, but for the most part they are synonymous with computable/non-computable.
3 or 7, truth or trust
“It is clear that ethics cannot be articulated.” – Ludwig Wittgenstein
Over the last few years I’ve been teaching and refining a series of lecture-workshops on Decision Making Under Uncertainty. Audiences include data scientists and mid-level managers working in corporates and public service agencies. The course is based on the distinction between uncertainties in which the variables are known and can be quantified versus those in which the variables are not known upfront and/or are hard to quantify.
Before going any further, it is worth explaining the distinction via a couple of examples:
An example of the first type of uncertainty is project estimation. A project has an associated time and cost, and although we don’t know what their values are upfront, we can estimate them if we have the right data. The point to note is this: because such problems can be quantified, the human brain tends to deal with them in a logical manner.
In contrast, business strategy is an example of the second kind of uncertainty. Here we do not know what the key variables are upfront. Indeed we cannot, because different stakeholders will perceive different aspects of a strategy to be paramount depending on their interests – consider, for example, the perspective of a CFO versus that of a CMO. Because of these differences, one cannot make progress on such problems until agreement has been reached on what is important to the group as a whole. The point to note here is that since such problems involve contentious issues, our reactions to them tend to be emotional rather than logical.
The difference between the two types of uncertainty is best conveyed experientially, so I have a few in-class activities aimed at doing just that. One of them is an exercise I call “3 or 7“, in which I give students a sheet with the following printed on it:
Circle either the number 3 or 7 below depending on whether you want 3 marks or 7 marks added to your Assignment 2 final mark. Yes, this offer is for real, but there a catch: if more than 10% of the class select 7, no one gets anything.
Write your student ID on the paper so that Kailash can award you the marks. Needless to say, your choice will remain confidential, no one (but Kailash) will know what you have selected.
3 7
Prior to handing out the sheet, I tell them that they:
- should sit far enough apart so that they can’t see what their neighbours choose,
- are not allowed to communicate their choices to others until the entire class has turned their sheets.
Before reading any further you may want to think about what typically happens.
–x–
Many readers would have recognized this exercise as a version of the Prisoner’s Dilemma and, indeed, many students in my classes recognize this too. Even so, there are always enough of “win at the cost of others” types in the room who ensure that I don’t have to award any extra marks. I’ve run the exercise about 10 times, often with groups comprised of highly collaborative individuals who work well together. Despite that,15-20% of the class ends up opting for 7.
It never fails to surprise me that, even in relatively close-knit groups, there are invariably a number of individuals who, if given a chance to gain at the expense of their colleagues, will not hesitate to do so providing their anonymity is ensured.
–x–
Conventional management thinking deems that any organisational activity involving several people has to be closely supervised. Underlying this view is the assumption that individuals involved in the activity will, if left unsupervised, make decisions based on self-interest rather than the common good (as happens in the prisoner’s dilemma game). This assumption finds justification in rational choice theory, which predicts that individuals will act in ways that maximise their personal benefit without any regard to the common good. This view is exemplified in 3 or 7 and, at a societal level, in the so-called Tragedy of the Commons, where individuals who have access to a common resource over-exploit it, thus depleting the resource entirely.
Fortunately, such a scenario need not come to pass: the work of Elinor Ostrom, one of the 2009 Nobel prize winners for Economics, shows that, given the right conditions, groups can work towards the common good even if it means forgoing personal gains.
Classical economics assumes that individuals’ actions are driven by rational self-interest – i.e. the well-known “what’s in it for me” factor. Clearly, the group will achieve much better results as a whole if it were to exploit the resource in a cooperative way. There are several real-world examples where such cooperative behaviour has been successful in achieving outcomes for the common good (this paper touches on some). However, according to classical economic theory, such cooperative behaviour is simply not possible.
So the question is: what’s wrong with rational choice theory? A couple of things, at least:
Firstly, implicit in rational choice theory is the assumption that individuals can figure out the best choice in any given situation. This is obviously incorrect. As Ostrom has stated in one of her papers:
Because individuals are boundedly rational, they do not calculate a complete set of strategies for every situation they face. Few situations in life generate information about all potential actions that one can take, all outcomes that can be obtained, and all strategies that others can take.
Instead, they use heuristics (experienced-based methods), norms (value-based techniques) and rules (mutually agreed regulations) to arrive at “good enough” decisions. Note that Ostrom makes a distinction between norms and rules, the former being implicit (unstated) rules, which are determined by the cultural attitudes and values)
Secondly, rational choice theory assumes that humans behave as self-centred, short-term maximisers. Such theories work in competitive situations such as the stock-market but not in situations in which collective action is called for, such as the prisoners dilemma.
Ostrom’s work essentially addresses the limitations of rational choice theory by outlining how individuals can work together to overcome self-interest.
–x–
In a paper entitled, A Behavioral Approach to the Rational Choice Theory of Collective Action, published in 1998, Ostrom states that:
…much of our current public policy analysis is based on an assumption that rational individuals are helplessly trapped in social dilemmas from which they cannot extract themselves without inducement or sanctions applied from the outside. Many policies based on this assumption have been subject to major failure and have exacerbated the very problems they were intended to ameliorate. Policies based on the assumptions that individuals can learn how to devise well-tailored rules and cooperate conditionally when they participate in the design of institutions affecting them are more successful in the field…[Note: see this book by Baland and Platteau, for example]
Since rational choice theory aims to maximise individual gain, it does not work in situations that demand collective action – and Ostrom presents some very general evidence to back this claim. More interesting than the refutation of rational choice theory, though, is Ostrom’s discussion of the ways in which individuals “trapped” in social dilemmas end up making the right choices. In particular she singles out two empirically grounded ways in which individuals work towards outcomes that are much better than those offered by rational choice theory. These are:
Communication: In the rational view, communication makes no difference to the outcome. That is, even if individuals make promises and commitments to each other (through communication), they will invariably break these for the sake of personal gain …or so the theory goes. In real life, however, it has been found that opportunities for communication significantly raise the cooperation rate in collective efforts (see this paper abstract or this one, for example). Moreover, research shows that face-to-face is far superior to any other form of communication, and that the main benefit achieved through communication is exchanging mutual commitment (“I promise to do this if you’ll promise to do that”) and increasing trust between individuals. It is interesting that the main role of communication is to enhance or reinforce the relationship between individuals rather than to transfer information. This is in line with the interactional theory of communication.
Innovative Governance: Communication by itself may not be enough; there must be consequences for those who break promises and commitments. Accordingly, cooperation can be encouraged by implementing mutually accepted rules for individual conduct, and imposing sanctions on those who violate them. This effectively amounts to designing and implementing novel governance structures for the activity. Note that this must be done by the group; rules thrust upon the group by an external authority are unlikely to work.
Of course, these factors do not come into play in artificially constrained and time-bound scenarios like 3 or 7. In such situations, there is no opportunity or time to communicate or set up governance structures. What is clear, even from the simple 3 or 7 exercise, is that these are required even for groups that appear to be close-knit.
Ostrom also identifies three core relationships that promote cooperation. These are:
Reciprocity: this refers to a family of strategies that are based on the expectation that people will respond to each other in kind – i.e. that they will do unto others as others do unto them. In group situations, reciprocity can be a very effective means to promote and sustain cooperative behaviour.
Reputation: This refers to the general view of others towards a person. As such, reputation is a part of how others perceive a person, so it forms a part of the identity of the person in question. In situations demanding collective action, people might make judgements on a person’s reliability and trustworthiness based on his or her reputation.’
Trust: Trust refers to expectations regarding others’ responses in situations where one has to act before others. And if you think about it, everything else in Ostrom’s framework is ultimately aimed at engendering or – if that doesn’t work – enforcing trust.
–x—
In an article on ethics and second-order cybernetics, Heinz von Foerster tells the following story:
I have a dear friend who grew up in Marrakech. The house of his family stood on the street that divide the Jewish and the Arabic quarter. As a boy he played with all the others, listened to what they thought and said, and learned of their fundamentally different views. When I asked him once, “Who was right?” he said, “They are both right.”
“But this cannot be,” I argued from an Aristotelian platform, “Only one of them can have the truth!”
“The problem is not truth,” he answered, “The problem is trust.”
For me, that last line summarises the lesson implicit in the admittedly artificial scenario of 3 or 7. In our search for facts and decision-making frameworks we forget the simple truth that in many real-life dilemmas they matter less than we think. Facts and frameworks cannot help us decide on ambiguous matters in which the outcome depends on what other people do. In such cases the problem is not truth; the problem is trust. From your own experience it should be evident it is impossible convince others of your trustworthiness by assertion, the only way to do so is by behaving in a trustworthy way. That is, by behaving ethically rather than talking about it, a point that is squarely missed by so-called business ethics classes.
Yes, it is clear that ethics cannot be articulated.
Notes:
- Portions of this article are lightly edited sections from a 2009 article that I wrote on Ostrom’s work and its relevance to project management.
- Finally, an unrelated but important matter for which I seek your support for a common good: I’m taking on the 7 Bridges Walk to help those affected by cancer. Please donate via my 7 Bridges fundraising page if you can . Every dollar counts; all funds raised will help Cancer Council work towards the vision of a cancer free future.
Learning, evolution and the future of work
The Janus-headed rise of AI has prompted many discussions about the future of work. Most, if not all, are about AI-driven automation and its consequences for various professions. We are warned to prepare for this change by developing skills that cannot be easily “learnt” by machines. This sounds reasonable at first, but less so on reflection: if skills that were thought to be uniquely human less than a decade ago can now be done, at least partially, by machines, there is no guarantee that any specific skill one chooses to develop will remain automation-proof in the medium-term future.
This begs the question as to what we can do, as individuals, to prepare for a machine-centric workplace. In this post I offer a perspective on this question based on Gregory Bateson’s writings as well as my work and teaching experiences.
Levels of learning
Given that humans are notoriously poor at predicting the future, it should be clear hitching one’s professional wagon to a specific set of skills is not a good strategy. Learning a set of skills may pay off in the short term, but it is unlikely to work in the long run.
So what can one do to prepare for an ambiguous and essentially unpredictable future?
To answer this question, we need to delve into an important, yet oft-overlooked aspect of learning.
A key characteristic of learning is that it is driven by trial and error. To be sure, intelligence may help winnow out poor choices at some stages of the process, but one cannot eliminate error entirely. Indeed, it is not desirable to do so because error is essential for that “aha” instant that precedes insight. Learning therefore has a stochastic element: the specific sequence of trial and error followed by an individual is unpredictable and likely to be unique. This is why everyone learns differently: the mental model I build of a concept is likely to be different from yours.
In a paper entitled, The Logical Categories of Learning and Communication, Bateson noted that the stochastic nature of learning has an interesting consequence. As he notes:
If we accept the overall notion that all learning is in some degree stochastic (i.e., contains components of “trial and error”), it follows that an ordering of the processes of learning can be built upon a hierarchic classification of the types of error which are to be corrected in the various learning processes.
Let’s unpack this claim by looking at his proposed classification:
Zero order learning – Zero order learning refers to situations in which a given stimulus (or question) results in the same response (or answer) every time. Any instinctive behaviour – such as a reflex response on touching a hot kettle – is an example of zero order learning. Such learning is hard-wired in the learner, who responds with the “correct” option to a fixed stimulus every single time. Since the response does not change with time, the process is not subject to trial and error.
First order learning (Learning I) – Learning I is where an individual learns to select a correct option from a set of similar elements. It involves a specific kind of trial and error that is best explained through a couple of examples. The canonical example of Learning I is memorization: Johnny recognises the letter “A” because he has learnt to distinguish it from the 25 other similar possibilities. Another example is Pavlovian conditioning wherein the subject’s response is altered by training: a dog that initially salivates only when it smells food is trained, by repetition, to salivate when it hears the bell.
A key characteristic of Learning I is that the individual learns to select the correct response from a set of comparable possibilities – comparable because the possibilities are of the same type (e.g. pick a letter from the set of alphabets). Consequently, first order learning cannot lead to a qualitative change in the learner’s response. Much of traditional school and university teaching is geared toward first order learning: students are taught to develop the “correct” understanding of concepts and techniques via a repetition-based process of trial and error.
As an aside, note that much of what goes under the banner of machine learning and AI can be also classed as first order learning.
Second order learning (Learning II) – Second order learning involves a qualitative change in the learner’s response to a given situation. Typically, this occurs when a learner sees a familiar problem situation in a completely new light, thus opening up new possibilities for solutions. Learning II therefore necessitates a higher order of trial and error, one that is beyond the ken of machines, at least at this point in time.
Complex organisational problems, such as determining a business strategy, require a second order approach because they cannot be precisely defined and therefore lack an objectively correct solution. Echoing Horst Rittel, solutions to such problems are not true or false, but better or worse.
Much of the teaching that goes on in schools and universities hinders second order learning because it implicitly conditions learners to frame problems in ways that make them amenable to familiar techniques. However, as Russell Ackoff noted, “outside of school, problems are seldom given; they have to be taken, extracted from complex situations…” Two aspects of this perceptive statement bear further consideration. Firstly, to extract a problem from a situation one has to appreciate or make sense of the situation. Secondly, once the problem is framed, one may find that solving it requires skills that one does not possess. I expand on the implications of these points in the following two sections.
Sensemaking and second order learning
In an earlier piece, I described sensemaking as the art of collaborative problem formulation. There are a huge variety of sensemaking approaches, the gamestorming site describes many of them in detail. Most of these are aimed at exploring a problem space by harnessing the collective knowledge of a group of people who have diverse, even conflicting, perspectives on the issue at hand. The greater the diversity, the more complete the exploration of the problem space.
Sensemaking techniques help in elucidating the context in which a problem lives. This refers to the the problem’s environment, and in particular the constraints that the environment imposes on potential solutions to the problem. As Bateson puts it, context is “a collective term for all those events which tell an organism among what set of alternatives [it] must make [its] next choice.” But this begs the question as to how these alternatives are to be determined. The question cannot be answered directly because it depends on the specifics of the environment in which the problem lives. Surfacing these by asking the right questions is the task of sensemaking.
As a simple example, if I request you to help me formulate a business strategy, you are likely to begin by asking me a number of questions such as:
- What kind of business are you in?
- Who are your customers?
- What’s the competitive landscape?
- …and so on
Answers to these questions fill out the context in which the business operates, thus making it possible to formulate a meaningful strategy.
It is important to note that context rarely remains static, it evolves in time. Indeed, many companies faded away because they failed to appreciate changes in their business context: Kodak is a well-known example, there are many more. So organisations must evolve too. However, it is a mistake to think of an organisation and its environment as evolving independently, the two always evolve together. Such co-evolution is as true of natural systems as it is of social ones. As Bateson tells us:
…the evolution of the horse from Eohippus was not a one-sided adjustment to life on grassy plains. Surely the grassy plains themselves evolved [on the same footing] with the evolution of the teeth and hooves of the horses and other ungulates. Turf was the evolving response of the vegetation to the evolution of the horse. It is the context which evolves.
Indeed, one can think of evolution by natural selection as a process by which nature learns (in a second-order sense).
The foregoing discussion points to another problem with traditional approaches to education: we are implicitly taught that problems once solved, stay solved. It is seldom so in real life because, as we have noted, the environment evolves even if the organisation remains static. In the worst case scenario (which happens often enough) the organisation will die if it does not adapt appropriately to changes in its environment. If this is true, then it seems that second-order learning is important not just for individuals but for organisations as a whole. This harks back to notion of the notion of the learning organisation, developed and evangelized by Peter Senge in the early 90s. A learning organisation is one that continually adapts itself to a changing environment. As one might imagine, it is an ideal that is difficult to achieve in practice. Indeed, attempts to create learning organisations have often ended up with paradoxical outcomes. In view of this it seems more practical for organisations to focus on developing what one might call learning individuals – people who are capable of adapting to changes in their environment by continual learning.
Learning to learn
Cliches aside, the modern workplace is characterised by rapid, technology-driven change. It is difficult for an individual to keep up because one has to:
-
- Figure out which changes are significant and therefore worth responding to.
- Be capable of responding to them meaningfully.
The media hype about the sexiest job of the 21st century and the like further fuel the fear of obsolescence. One feels an overwhelming pressure to do something. The old adage about combating fear with action holds true: one has to do something, but the question then is: what meaningful action can one take?
The fact that this question arises points to the failure of traditional university education. With its undue focus on teaching specific techniques, the more important second-order skill of learning to learn has fallen by the wayside. In reality, though, it is now easier than ever to learn new skills on ones own. When I was hired as a database architect in 2004, there were few quality resources available for free. Ten years later, I was able to start teaching myself machine learning using topnotch software, backed by countless quality tutorials in blog and video formats. However, I wasted a lot of time in getting started because it took me a while to get over my reluctance to explore without a guide. Cultivating the habit of learning on my own earlier would have made it a lot easier.
Back to the future of work
When industry complains about new graduates being ill-prepared for the workplace, educational institutions respond by updating curricula with more (New!! Advanced!!!) techniques. However, the complaints continue and Bateson’s notion of second order learning tells us why:
- Firstly, problem solving is distinct from problem formulation; it is akin to the distinction between human and machine intelligence.
- Secondly, one does not know what skills one may need in the future, so instead of learning specific skills one has to learn how to learn
In my experience, it is possible to teach these higher order skills to students in a classroom environment. However, it has to be done in a way that starts from where students are in terms of skills and dispositions and moves them gradually to less familiar situations. The approach is based on David Cavallo’s work on emergent design which I have often used in my consulting work. Two examples may help illustrate how this works in the classroom:
- Many analytically-inclined people think sensemaking is a waste of time because they see it as “just talk”. So, when teaching sensemaking, I begin with quantitative techniques to deal with uncertainty, such as Monte Carlo simulation, and then gradually introduce examples of uncertainties that are hard if not impossible to quantify. This progression naturally leads on to problem situations in which they see the value of sensemaking.
- When teaching data science, it is difficult to comprehensively cover basic machine learning algorithms in a single semester. However, students are often reluctant to explore on their own because they tend to be daunted by the mathematical terminology and notation. To encourage exploration (i.e. learning to learn) we use a two-step approach: a) classes focus on intuitive explanations of algorithms and the commonalities between concepts used in different algorithms. The classes are not lectures but interactive sessions involving lots of exercises and Q&A, b) the assignments go beyond what is covered in the classroom (but still well within reach of most students), this forces them to learn on their own. The approach works: just the other day, my wonderful co-teachers, Alex and Chris, commented on the amazing learning journey of some of the students – so tentative and hesitant at first, but well on their way to becoming confident data professionals.
In the end, though, whether or not an individual learner learns depends on the individual. As Bateson once noted:
Perhaps the best documented generalization in the field of psychology is that, at any given moment, the behavioral characteristics of a mammal, and especially of [a human], depend upon the previous experience and behavior of that individual.
The choices we make when faced with change depend on our individual natures and experiences. Educators can’t do much about the former but they can facilitate more meaningful instances of the latter, even within the confines of the classroom.
Eight to Late 