Strings Being Pulled and Other Challenges

My previous posts have talked about how different demographics seem to experience fairness and collaboration differently in their social-impact networks. According to my survey, network stakeholders in general do experience their networks as fair and collaborative spaces, but a few areas for improvement have emerged.

In my survey, I asked respondents to rate their level of agreement with various statements that have to do with whether processes for making decisions and allocating resources are fair. The tool I used for this is the Process Quality Scale, which has 15 such statements and a validated scoring method. Feel free to reach out for more information about my methods and interpretation, bu for the sake of this blog, I'll cut to the chase. One last word before I do: don't forget that most respondents actually do experience their networks as fair (at least as measured by the PQS), so what's written below is in the interest of continuous improvement, and not meant as an indictment.

Strings Pulled from the Outside and Decisions Made in Advance

About 35% of the 85 respondents had some level of agreement with the statement that "often decisions are made in advance and simply confirmed by the process."  Meanwhile, about 30% had some level of agreement that "strings are being pulled from the outside, which influence important decisions."

A large minority of respondents felt strings are often pulled from the outside.

Some People's Merits Are Taken for Granted

When presented with the statement that "some people’s “merits” are taken for granted while other people are asked to justify themselves," roughly 30% of respondents had some level of agreement, though only 10% stated they fully agreed.

The idea that some people's merits are taken for granted while others have to justify themselves reminded me of George Orwell's Animal Farm, where the animals overthrew their human overlords, only to have new inequalities emerge.

Gender Differences

As I mentioned in my posts with more detailed survey results on fairness, men tend to feel more strongly than women do that processes are fair in their networks. However, it's not clear that this difference strongly impacts on personal decisions to collaborate actively in a network. For example, 90% of the 41 women who responded to a question about whether they actively participate in their network said that they did. Only 78% of the 28 men who responded that question said the same.  More on this in future posts.

What to Do about It?

I recently had the opportunity to connect in-person with leaders and advisers for five different networks. We sat down together for a bit over an hour to talk about how to interpret these results and what might be done to improve things. Stay tuned for another blog post on this topic mid-week! 

Who feels the collaboration?

In my last post, I shared the subset of survey results concerning fairness in collaborative networks. In this post, I share some preliminary findings regarding collaboration: whether stakeholders feel its happening, whether they think it improves outcomes, whether they feel the network shares a common goal, and whether they themselves feel they participate in the network.

More good news

As with perceptions of fairness, the overwhelming majority of respondents to the survey feel their networks are collaborative spaces, that the collaboration makes them more effective, and that everyone is working toward a shared goal. The statements presented to respondents were adapted from ones administered to the RE-AMP Network by Peter Plastrik and Chinwe Onyeagoro as part of an outside evaluation a few years back. Because I wanted to be able to tie people's self-assessed level of participation to other aspects of the survey, I added a question asking people if they actively participate. Most respondents do see themselves as active participants in the network. 

The bar chart below shows the percent of respondents from each demographic that said they "strongly agree" with the statement about collaboration, later I'll show you a broader spectrum of data. 

Percent of respondents who strongly agree with statements about collaboration in their network.
*While we generally want a sample size of at least 30 for statistical significance, only 28 men responded to the bottom three statements, and only 27 responded to the top statement. The sample size for those in a racial minority is 12, and for men who are a gender minority, the sample size is 10.

As you can see (or maybe you can't if you are looking at this on a mobile device), about a third of all respondents strongly agree that the network collaboration helps them be more effective. Slightly more than that strongly agree that they have a shared purpose. More still strongly agree that they have a highly collaborative experience in the network, though some interesting differences between demographics appear.

A gender distinction

As with perceptions of fairness, the subgroups of respondents that seem to be the most different are men and women. I don't want to make too much of this, since we can see that both groups seem to view their networks as collaborative places, but the subtle difference is quite interesting. Take, for example, the statement about the network being a highly collaborative place. 

In the bar chart below, we see that nearly 60% of men strongly agree with the statement, compared to just under 40% of women. However, 100% of women at least agree more than disagree with the statement, whereas only about 85% of men have some level of agreement. Although this particular question is the one where this pattern shows up most strongly, it holds for all the responses: men are more likely than women to either strongly agree or to disagree with the statement.

This is where the small sample size really becomes a challenge. When we are talking about only 28 men altogether, then it's hard to say if this is just a data blip, or if it is pointing us toward something useful. In the coming weeks I will be hosting some group conversations with network practitioners to try to understand if it's productive to dig into these differences. I will also be looking at the open-ended responses from the survey for clues to the direction members want their networks to take. Do feel free to reach out to me with your own interpretation as well.

Early Results of Process Quality Survey in Collaborative Networks

This summer I put out a call for people engaged in collaborative networks to take a survey about their experiences in those networks. Thank you to everyone who responded and who encouraged others to do the same. Although I may not be able to use the results in this round of research, you can still take an abbreviated version of  the survey if you like.

Below you can see some of the demographics of who responded to the survey. You will see that the answers don't always add up perfectly, that's because most of the questions did not require a response and some people chose not to respond.

Survey respondents' demographic info

You probably notice a lot of the same things I did when looking at the demographics. For example, the high number of responses from women (which is particularly noteworthy since nearly half of respondents said men and women were present in roughly equal numbers in their network), and the small number of respondents who are part of a racial minority in their network (of those 12 only 2 identified as white). Also, if you are like me, you are developing theories about why the demographics shook out as they did.

In this post, I will share some preliminary analysis of the quantitative data that was gathered regarding fairness. My next post will examine the quantitative data on collaboration. Future posts will explore open-ended responses, as well as dig more deeply into some of the interesting patterns that emerge.

Some words of caution: In general, a sample size of 30 or more is considered statistically valid. However, as you can see, we only barely have a statistically significant sample size of men and we do not have a statistically significant sample size for people who are in a racial minority in their network, nor for those in the governmental sectors. Moreover, those of us working in this field know that sometimes the term "network" is interpreted very differently by different people. I tried to filter for this by asking people entering the survey if they met certain criteria, but it's still hard to say how much "apple-to-apple" comparisons are really embedded in these results.

Lastly, because I want this blog to be readable, I am mainly avoiding detailed discussion of methodology, but please do reach out if you want more information about how I arrived at the interpretations below.

So, if you are willing to hold all these caveats and treat these results lightly, let's dig in!

Procedural Fairness 

The survey used the Process Quality Scale (PQS) developed by Darrin Hicks and colleagues to measure perceptions of how fair a process is (as opposed to how fair the outcomes of a process are, see my earlier blog post). This 15-question questionnaire deals with whether people see the process as authentic, revisable, and consistently applied. In the survey, I asked people to base their responses on their impressions about processes in their network overall, as opposed to one particular process.

The good news

In general, people find their networks to be engaged in processes that are mainly fair. At least, insofar as the PQS can measure such a thing. In 8 of the questions, the most common response was that processes are more fair than not, and in the other 7 questions, the most common response was that the processes are fair. 

I went into this analysis curious to see if a strong difference would be noted between members and staff/consultants. However, the results were virtually indistinguishable. Members seems to think their networks are a pretty fair place to be, and so do the staff and consultants. 

The interesting news

You don't have to be a novelist to know that readers don't want to hear about all the happy things, readers want the dirt! Not you, of course, but perhaps the other readers of this blog. Well, it turns out that when we break the data down, we see that the intensity with which people perceive procedural fairness varies significantly. 

I was surprised to see that the group that seems to have the strongest sense of fairness in their networks was men who are in a network that is majority-woman. The second strongest perceptions of fairness come from those who are in a racial minority in their network, although this group had a wider range of opinion than the men who were in gender minorities. 

Bubble chart showing the modal average responses to the PQS, broken down by subgroups

Women and men are the two groups that have the most pronounced differences in interpretations of the level of fairness. In 13 of the questions on the PQS, the most common response from men was that the process was fair. For women, they said the process was fair in response to only 3 questions, and for 11 others they said it was only more fair than not. To one question, the most common response from women was that things were more unfair than not (the question was about whether everyone has equal opportunity to influence decisions). 

I suspect the relatively strong feelings of fairness among racial minorities may have something to do with the fact that 75% of that group are men, and of those about half are in a network comprised largely of women. A larger sample size might reveal significantly different answers. As it is, respondents who were a racial minority were still the only group to have more than one question generate a response that things were more unfair than not (one question was about giving some more than they deserve while shortchanging others, another was about some people's merits being taken for granted while others have to justify themselves). 

As you can see, there is a lot to unpack. So stay tuned, and, if you want to see all the questions, feel free to go to the abbreviated version of the survey here. 

Interpretation

As I move forward in the research, I will be examining the other data and engaging in interviews to attempt to understand the differences in particular between men's and women's perceptions. Is it possible that women are more accustomed to collaboration than men, and thus situations that seem incredibly collaborative to men are merely acceptably collaborative to women? Could men be obliviously on the receiving end of deferential treatment? Could women be consulted less, or expected to pick up more of the procedural work? Is the fact that it was more challenging to get men to take the survey somehow related?  Most importantly, is it a problem that there is a discrepancy in the answers of men and women, and can this data help us make networks better? Drop me a line if you want to be part of the conversation!

Procedural and Distributive Fairness

In this post, I return to the topic of Fairness Heuristic Theory, and make a key distinction between two ways fairness can be experienced: in an outcome or in a process.

What's a Heuristic and Why Should I Care?

As it's used here, think of a heuristic as a rule of thumb, one that allows you make a decision without having to think through every angle. You know it's imperfect, but you use it to help you make sense of what's going on and make judgements fairly quickly. I once met a woman who would only date a man who had an iPhone. To her, Androids represented slovenliness, lack of ambition, and a willingness to inconvenience others. By focusing on iPhone users, she was able to give her attention to just a few men, one of whom she fell head over heels for and married.

In the case of Fairness Heuristic Theory (FHT), an individual's perceptions of fairness are used as a rule of thumb to guide their decisions about whether to collaborate with others. Unlike the case of the iPhone-phile above, the rule of thumb operates in the background, not usually something that is explicitly thought about. To understand why it matters for networks, we need to understand the difference between procedural and distributive fairness (also called procedural or distributive justice). 

Procedural and Distributive Fairness

When we think about whether things are fair, many of us think right away about outcomes. But research by E. Allan Lind, Kees Van den Bos and others indicates it's not usually that type of fairness that influences our decisions about whether to join with others. Rather, we look for clues about whether we can trust the process, how we can expect to be treated, and what our standing in the group will be.

This classic union cartoon from the second half of the last century demonstrates the concept of distributive (un)fairness.

Distributive fairness is about who gets what (funding, credit, blame, promotions, exposure, etc.). Procedural fairness is about how the getting gets done.

Whereas this one has to do with procedural (un)fairness

Research by D. Hicks and C. Larson indicate that a process is generally perceived as fair when it is seen as authentic, revisable, inclusive, and transparent. (Want to share how your network measures up? Take my research survey.)

Which Is More Important?

Neither type of fairness is intrinsically more important. But there is reason that our perceptions of procedural fairness generally influence willingness to join collaborations more than distributive fairness: namely, we are usually exposed to process before outcome. 

Research by Lind and colleagues has shown that we tend to form opinions about fairness very quickly, and that, once formed, those opinions are pretty stable. In other words, if our first exposure to a network is fair, then things that happen later in that network will tend to be interpreted through the understanding that the network is basically fair. Things that might otherwise be seen as unfair will either be explained away or be seen as an aberration. Conversely, if our first experience is perceived as unfair, then later experiences will be interpreted with the understanding the network is a basically unfair place. 

As a general rule, potential network members are presented first with options for participation (i.e. processes for collaborating to share knowledge, co-create strategy, experiment together, etc.). Rarely (but not never) a member is invited to a network by being presented with an outcome, such as a portion of funding or some other resource.

Because the first exposure to a network is usually a process, procedural fairness is usually a stronger force in a member's overall opinion about it. At least, if FHT holds true.

Does It Actually Matter?

I've been interviewing thought leaders in the world of social-impact networks. Some of them feel the issue of fairness is an important one that affects their networks, others feel members typically are not affected by a desire for fair treatment either in terms of process or outcome, but rather join networks for altruistic reasons.  

What do you think? Leave me a comment or take my research survey. 

Commodities of Power in a Collaborative Network

In my last post, I invited readers to take a survey about their experiences with collaboration in their networks. The survey is still open and I encourage you to take it if you haven't yet! In this post, I discuss what I am learning about "commodities of power" in collaborative networks. A commodity of power is something that demonstrates prestige and standing.

In his book Learning for Action, Peter Checkland gives an example of an organization where people were divided into "KT" and "NKT." The "KT" people "Knew Tom," who was the founder. The "NKT" people "Never Knew Tom" and were relegated to a lesser role. Having known Tom was a commodity of power.

I asked a group of network practitioners the following question: If I were an alien from outer space who didn't know anything about humans, what is the decoder ring you could give me so that I know who has power?

If I were an alien, how could I know who has power and who doesn't?

  

Right away, everyone listed formal roles. Someone on the steering committee has more power than a member not on the steering committee, for example. As the conversation progressed, people were able to give my imaginary alien the keys to decoding much more subtle ways that power is understood in their networks. Examples of commodities of power included:

• Convening a conversation or meeting
• Other members do the work of your vision
• Having exposure, such as by speaking or presenting
• Control over the budget
• Access to decision-makers
• Being eloquent
• Having a firm commitment to one's values
• Conversely, being in a position to accuse someone of not living up to shared values
• Being tattled to (in other words, the person that someone complains to in order to correct someone else's behavior)

The point of learning about commodities of power is that it helps one frame interventions. If recommendations are going to run counter to prevailing forces, it's best to understand that up front. 

What do you think? Leave a comment sharing some of commodities of power at work in your network, or take the research survey that will ask you about this and other factors relevant to collaboration. 

Early Exploration into Network Culture

Are you someone with a perspective on a particular social-impact network? Please take my survey regarding collaboration. In previous posts I gave brief overviews of my research methodology and topic. In this post, I invite you to contribute to the research. I also explain a bit more about what I am doing in this phase of the research and share what I've been learning so far.

A Survey to Find out about Networks

I am conducting a survey in order to find out more about how those involved with networks experience collaboration. The heart of the survey is the Process Quality Scale, which I was introduced to when Darrin Hicks, a Communications professor from Denver University, presented it at the Network Leadership Training Academy. This scale is used to measure whether participants in collaboration feel the process is fair and authentic. I am using it in this survey to help those of us in social-impact networks develop a rough baseline for ourselves in this regard, so that we can recognize our challenges, and take actions to improve. 

The survey also draws on suggestions from Pete Plastrik, Madeleine Taylor, and John Cleveland's Connecting to Change the World in ways that will tie results from the PQS more closely to a network-specific form of collaboration. Additionally, a simplified version of Werner Ulrich's Critical System Heuristics is employed in order to help identify power and impact within networks (perhaps this warrants a blog post of its own down the pike).

But don't worry! You don't need to know anything about PQS or heuristics or any other jargon to take the survey!  All the questions are in plain English, and it will take about 15-20 minutes to share your thoughts. Please also share the link widely with other network practitioners, particularly those who are network members. Here's the link for easy cut-and-paste: https://www.surveymonkey.com/r/FG783WS

What I Am Finding Already

Prior to setting up this survey, I spent a good bit of time interviewing various network practitioners (a task that will be ongoing throughout my research), as well as acknowledging my own perspective. 

In Soft Systems Methodology, the first step is to find out about a problematic situation. This typically involves drawing rich pictures, and analyzing the roles, values, norms, and commodities of power in a situation. 

A rich picture is one that is hand drawn by someone concerned with the situation. It expresses the people, relationships, structures, and processes graphically, usually metaphorically. 

For example, when I drew a rich picture (below) to describe collaboration in social-impact networks, I imagined 3 people, each with expertise in one of the following: mountaineering, sailing, and desert travel. They stood together at a fork in the road. Should they each individually pursue the fairly straightforward path to simple fixes that only address a symptom of the root problem? Or should they combine forces and go on a long and confusing trip, in the belief that they could ultimately unlock systems change? Up front, they know that the journey involves deserts, oceans, and mountains, so by working together they should be able to get there. (If I were to redraw this picture, I would show that one typically doesn't need to make quite so stark a choice, as most in a network work at both the immediate and long-term scale.)

My rich picture of collaboration in networks

As they travel, their collaboration creates new emergent properties (indicated by a poorly-drawn mushroom, which is the emergent property of mycorrhizae and the environment). For example, meals and music are emergent properties of their collaboration, since they cook together and sing while walking. Also patience is an emergent property, when one member falls ill and they must wait and care for him. As they continue, new and unforeseen terrain arrives: a forest, which none of them have experience navigating. Now all the practice they got by collaborating to make meals and music gives them confidence that they can collaborate to create emergent strategies that will get them through the woods and that much closer to systems change. 

As you can see, rich pictures need not be beautiful, nor even understandable without detailed explanation. Their main utility arises from accessing a different part of your brain than the one that tries to describe things using words. This enables all those involved in the inquiry to find out what they themselves think (and feel) about a situation, and to then share with others. 

Recently, I asked a group of network practitioners to draw and share rich pictures about collaboration in their networks. Due to my limited instruction, most simply drew diagrams of how one component of their network related to others (which was still quite informative!), but a few metaphors emerged: a prickly box full of uncooperative legislators, and a big house full of all kinds of people, for example.

The conversation with these practitioners grew much more interesting when I began to ask about roles in driving collaboration. Most practitioners felt that staff act as "curators" of collaboration, by keeping track of member needs and requests, and responding to what seems most beneficial. Members with more capacity and expertise also tend to drive the shape of the collaboration, in these practitioners' experience, making me wonder if there might be a "success to the successful" archetypal pattern at work (again, something for another blog post down the road!). 

To find out about values in the networks, I asked practitioners to share gossip. I asked what members say about each other if they want to praise someone or criticize someone. I promise, we didn't use any names! Some interesting themes emerged. Lots of praise or criticism stemmed from ideas about what's strategic. In many networks, someone might criticize an organization working at a different scale than themselves by saying that scale of work isn't strategic. Other times, someone might say that the organization's purpose isn't strategic and they shouldn't even exist. 

On the other hand, sometimes those that seemed to have a good clear strategy are seen as not being very good collaborators. When members of these networks wanted to praise one another, they often talked about how an organization really "shows up." Whereas someone might be criticized as "in it for themselves."

Popularity itself was not seen as necessarily that valuable. More than one practitioner said they often see examples of members who are genuinely liked by everyone, but not really respected or seen as valuable in the network because they simply don't get things done. Accountability, then, was another key to being valued. However, it was useful to note that being disliked by the "right" people could be valuable. For example, if someone working at one scale was actively disliked by people working a scale seen by peers as "nonstrategic," then that dislike might lead that person to be valued.

Although it's too early to draw conclusions, at this stage it looks like any recommendations for improving collaboration in networks should be seen as something that helps members see themselves and each other being strategically placed, productive, and able to get things done. 

I will continue finding out about networks through observation, interview, workshops, and of course, the survey, which I hope you will take and share with others. All of this will allow us to recognize our starting point for improvement, so that any recommendations for improvement can be grounded in members' lived experience. 

Commodities of Power

In my next post, I will share what I've been learning about commodities of power in a network. These are status symbols, or ways of demonstrating who has influence. You can sign up for updates in the upper right corner of this post (note, this feature wasn't working properly before, so if you signed up earlier, please try again.)

Fairness Heuristic Theory & Networks

In my last post, I discussed Soft Systems Methodology, which I am using to explore the role of fairness in collaborative networks. In this post, I share the basic premise of Fairness Heuristic Theory. In my next post, I will talk about what I have been learning about how network leaders view their network culture.

Fairness Heuristic Theory

E. Allan Lind and his colleagues first proposed the Fairness Heuristic Theory (FHT) in 1992. In brief, the theory is that perceptions of fairness act as a "rule of thumb" for measuring whether one should invest in a collaborative effort. If one perceives that fairness will reign within that collaboration, one is vastly more likely to to lend one's resources and--more importantly--one's identity to the effort, according to this theory.

According to Lind, the most essential aspects of people's perception of fairness are rooted in whether one feels valued and has standing as part of the group. They render this judgment based on feelings of inclusion, ability to effectively raise concerns, and sensing respect in the way others treat them. In cases where the person feels they have standing and will be treated fairly, they will willingly become part of a whole.

Fairness Heuristic Theory in Collaborative Networks

FHT comes out of organizational science, and particularly explores how employees might become willing collaborators in the plans of their boss. Why, for example, should someone put in long hours at work rather than please themselves with more spare time? Why should they do a job very well rather than do what is easiest for them personally? Fair treatment (including a sense of standing in the group), Lind and others argue, is a significant part of the answer.

But the tension between the part and the whole is, I argue, at the heart of collaborative networks as well. A network is not a top-down structure giving marching orders to soldiers, it is a bottom-up gathering of diverse perspectives on a complex problem. Members must autonomously agree to collaborate to achieve a purpose, even while their own individual purpose is distinct.

Consider the example below, in which a network has formed to end malnutrition. Member A experiences the part/whole tension in that this organization (which focuses on childhood malnutrition) must be willing to lend its identity and resources to some efforts that are beyond the scope of its own concern: for example ending malnutrition among the geriatric population. Members B and C experience the part/whole tension differently, in that their mission encompasses many things beyond the scope of the network. In this sense, the network competes with other valuable work they might do.  In all three cases, there may be times that the good of the whole network actually runs counter to the member purpose: for example perhaps in some cases, increasing arable land decreases rather than increases hunger--putting Member B in a dilemma.

Now, when these members are asked to participate in the network, they are confronted first not with an outcome, but with the question of participating--that is, collaborating. They must decide whether, in essence, to write a check that consists of their identity and resources for a product (the end of malnutrition) that is a very long way from delivery. They could choose not to participate, or, as those of us in collaborative networks know very well, they might just to "participate" just enough to stay on the mailing list so they stay in the know, but without truly lending their identity and resources to the network's purpose. But they just might choose to participate at a very high level, which, as it happens, is the only way they can achieve the shared purpose.

Unlike an organizational setting, there is not a strong authority figure who can compel at least some level of participation. Members must decide to do it entirely themselves, making fairness even more essential.

The point of my research is to test perceptions of fairness among network stakeholders and to develop feasible recommendations to improve perceptions of fairness. Ideally, subsequent research would revisit both the desirability and feasibility of implementing them, and determine if resultant improved perceptions of fairness do indeed improve member willingness to collaborate deeply.

I will return to Fairness Heuristic Theory later, but my next post will share what I am learning already from network leaders about their network culture. This is important because, without understanding the landscape, it would be very challenging to make recommendations that are both relevant and feasible.  

Using Soft Systems Methodology to Explore Networks

Welcome to my blog on systems thinking in collaborative networks. I am interested in a wide range of systems thinking as it relates to network practice, and my current research focuses specifically on the role of fairness in collaboration. This research is being conducted to fulfill the requirements of Masters in Science from the Open University's Systems Thinking in Practice program. The OU is based in the UK, but I am in the US.

In this post, I share my choice of methodology, Soft Systems Methodology, and why I believe it is suited to collaborative networks. In my next post, I provide an overview of Fairness Heuristic Theory and its potential application to networks.

Soft Systems Methodology (SSM)

Soft Systems Methodology (SSM) was developed in the 1980s by Peter Checkland. It has evolved  into a modern form with four stages: 1) finding out about a problematic situation, 2) formulating purposeful activity models, 3) using the models for debate and reaching accommodation among stakeholders, and 4) taking action to improve the situation.  However,  these steps do not necessarily happen in the order listed, or only one time. An SSM intervention is iterative.

Source: Giles A. Hindle in Case Article—Teaching Soft Systems Methodology and a Blueprint for a Module

Why "soft" systems? A key insight Checkland contributed with this methodology was that truly complex situations are not "real" or "hard" in the sense that they can be objectively defined or engineered. A complex system is not simply an extremely complicated technical problem, but is confounding in that it's defined differently, depending on your point of view. The boundary of the system, the output of the system, and the dynamics are dependent on perspective. The role of perspective is bound inextricably with complexity. 

For example, if you point to a truck and ask different people with different points of view the main thing that truck produces,  you might receive any of the following answers: transportation, ability to haul, pollution, prestige, amelioration of a mid-life crisis, or a set of bills. Imagine the truck is jointly owned by people who hold all these perspectives. If the truck stops working, different people will have very different reactions to this, and understanding the complicated mechanics of the truck will only get you so far in being able to improve the problem.

I like Hindle's diagram (above) because it puts the role of multiple perspectives at the center of the SSM intervention. A key part of finding out about the problem is enabling diverse stakeholders to define how they see the situation and the context it sits within. One technique for this is drawing pictures that represent what is happening in the system: the structures, the processes, the relationships. Participants also consider the roles, norms, and values at work in the situation.

Once one has made a good start on finding out about the situation, the group engages in thought experiments to make models of activities that are relevant to the situation, and they use these as a basis for coming to an agreement on how to improve things. These discussions are based not only in what would be desirable, but also what is feasible, considering the relationships, roles, norms, and values already present in the situation.

The final stage of the methodology is taking action, which results in a new and (hopefully) improved, but (likely) still problematic situation, which needs to be explored. This may sound like embracing Sisypheanism, but it's quite different. It's simply recognizing that in complex situations, today's problems are often the result of yesterday's solutions. Hopefully today's problems aren't as bad as yesterday's problems were, because hopefully we have intervened wisely. But if a single set of actions could solve the problem, it probably wasn't so complex to begin with. In SSM, we have a tool for managing complexity, rather than for being a superhero who solves everyone's problem.

SSM and Collaborative Networks

I draw from many different fields of Systems Thinking in my work with Networks, but I find  SSM particularly well-suited to networks because SSM embraces the role of multiple perspectives throughout the entire process. 

SSM is also focused as much on learning as it is on outcomes. Collaborative networks are still emerging as a way of impacting complex situations, and we have so much to learn! By using a methodology that is grounded in learning, we reap both the benefits of improvements we undertake, and we also learn how to learn--collaboratively. 

In my next post, I will talk about Fairness Heuristic Theory and why I am using SSM to explore its application to collaborative networks.