A Seven-Step Primer on Soft Systems Methodology

I'm currently TAing for CSC2720H Systems Thinking for Global Problems, a graduate-level course on systems thinking. In class today we talked about soft systems thinking (SSM), an approach which uses systems thinking to tackle what are called "wicked problems". I thought I'd outline one approach to SSM, as it's useful to CS education research.


Step 1: Identify the domain of interest

Before you can research something, you should first decide what your domain is. What topic? What system are you studying? For example, "teaching computer science" could be your starting point, as could "climate change".

Chances are you're looking at a wicked problem. Conklin's definition of wicked problems are that:

1. The problem is not understood until after the formulation of a solution.
2. Wicked problems have no stopping rule.
3. Solutions to wicked problems are not right or wrong.
4. Every wicked problem is essentially novel and unique.
5. Every solution to a wicked problem is a 'one shot operation.'
6. Wicked problems have no given alternative solutions.

Because you're looking at a domain which doesn't have a clear definition or boundaries, you'll first want to immerse yourself in the domain. One trick is to draw "rich pictures", which are essentially visualized streams of consciousness.

You should also think about what perspectives you bring into this domain. What biases and privileges do you have going into this? Why are you interested in this domain? What do you have to gain or lose here?

On the Social Psychology of Sexism

As somebody interested in gender equality in CS, one thing that's proved quite illuminating for me is to read up on the psychology of sexism. Why does sexism persist in society? What social and psychological structures keep it in place?

Sexism in some ways is unlike other forms of discrimination. When it comes to race, or class, or disability, the social psychology literature will frequently talk about social distance. But when it comes to women, men "can't live without 'em" [1] -- and so things tend to be a bit different.

Ambivalent Sexism

It turns out that sexism has two faces: good old hostile sexism -- and the more palatable benevolent sexism. Benevolent sexism is the notion that women are wonderful, caring, nurturing and beautiful creatures -- and so must be protected and provided for. (Note the "creatures" -- not "people".)

The evidence on the psychology of sexism is that the people who espouse hostile sexism are also benevolently sexist. They think women shouldn't work -- women should stay home and care for the children because women are so good at mothering. And they get hostile when their regressive worldview clashes with attempts of women and men to change the status quo.

Furthermore, the more you're exposed to benevolent sexism, the more likely you are to later take on hostile sexist views. The more you're primed to believe that women should fill the magical-traditional role, the more likely you'll try to thwart any attempt to move away from this view.

A vital thing to note about benevolent sexism is how frequently it is embraced by women. Many women will argue that women are better at being nurturing, or communicating, etc. After all, why would you turn down a worldview that argues you're wonderful and worthy of protection?

Toward a systems model of CS enrolments

This term, I'm taking my supervisor's grad course on "Systems Thinking for Global Problems". It's been quite interesting so far. In our last couple of lectures, we have been talking about feedback loops.

And with that on my mind, I was particularly struck by a recent post on Mark Guzdial's blog reposting a keynote by Eric Roberts:

[in response to increasing CS enrolments], 80% of the universities are responding by increasing teaching loads, 50% by decreasing course offerings and concentrating their available faculty on larger but fewer courses, and 66% are using more graduate-student teaching assistants or part-time faculty. [...] However, these measures make the universities’ environments less attractive for employment and are exactly counterproductive to their need to maintain and expand their labor supply. They are also counterproductive to producing more new faculty since the image graduate students get of academic careers is one of harassment, frustration, and too few rewards. 

Computer science departments have, for decades, had cyclical enrolment. The sort of oscillation in enrolments is exactly the sort of thing you see in systems analysis when you have a balancing feedback loop with a delay in it.

Subtyping, Subgrouping, and Stereotype Change

There's been a fair bit of research finding that negative stereotypes are part of what deters women and racial minorities from computer science and STEM in general (e.g. [1]). These stereotypes make it harder for women and minorities to personally identify with computer science, and amplify some of the biases that they face in CS. So for this post, I'll be going over observed phenomena in social psychology and sociology that pertain to stereotype change.

Stereotypes

Stereotypes are really hard to change. They're reinforced from many sources (media, individuals, groups, etc). But even more than that, stereotypes are schema: they are how we mentally organize information about social groups, and how we can determine whether we are "in" or "out" of a group. Schema allow us to process information effortlessly, and are pretty deeply ingrained once they're there.

The human brain is not very good at changing schema. When we see evidence that contradicts our schema, our brains will do all sorts of mental gymnastics to avoid confronting or changing the incorrect schema. Most frequently, we forget that we saw it all. Sometimes our misconceptions even get stronger [2].

This happens with stereotypes. Betz and Sekaquaptewa did a study where they showed role models to young girls, to try to motivate the girls' interest in STEM [3]. Role models were either gender-neutral, or feminine. The result? Gender-neutral role models boosted interest -- and feminine (counterstereotypic) role models actually reduced girls' interest in science. To these girls, the feminine scientist -- a stereotype violator -- is aberrant.