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Arnold_et_al-2017-CompleteSetSystemsThinkingSkills.pdf

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 ABSTRACT This paper proposes a complete set of systems thinking skills for use across many different disciplines. The paper places particular emphasis on the ability to assess each of the skills quantitatively, a comprehensible description of the skills, and the completeness of the set. The proposed skills derive from a review of the literature, the application of systems thinking experience, and the ap- plication of systems thinking to itself. Several different sets of systems thinking skills exist throughout the systems community, but common key concepts distill from these sets. When we consider combinations of these concepts separately, holistically, and together as a system, a single, cohesive set of skills emerges. Systems thinking is widely believed to be of critical importance across many different fields; some say that skillful use of sys- tems thinking skills could have prevented such disasters as World War II, the Great Depression, and the Challenger space shuttle disaster, as well as lessened or avoided the effects of many major environmental disasters. At the opposite send of the spectrum, systems thinking can enhance health care, improve the economy, improve technology, laws, international and interpersonal rela- tionships, schools, organizations, and so much more. However, this very useful skill set still lingers outside mainstream education. To address this problem requires a set of assessable, comprehensible systems thinking skills. This paper defines, describes, and details such skills.

Ross D. Arnold, [email protected]; and Jon P. Wade, [email protected]

A Complete Set of Systems Thinking Skills

BACKGROUND

The skills proposed in this paper are an extension of a definition of systems thinking proposed by Arnold and Wade (2015). Arnold

and Wade define systems thinking as a system of synergistic analytic skills used to improve the capability of identifying and understanding systems, predicting their behaviors, and devising modifications to them in order to produce desired effects. This definition is backed by a thorough literature review as well as the System Test concept also proposed in the paper (Arnold and Wade 2015). The definition includes a Systemigram that describes the various interacting pieces of systems thinking. The Arnold and Wade definition, as well as the skills proposed in this paper, are part of a research effort to define, measure, and as- sess systems thinking. This effort supports a broader effort to expand the reach of systems thinking and systems engineer- ing in general, including research using simulation as a way to accelerate learning in systems engineering (Zhang, Bodner, Turner, Arnold, and Wade, 2016).

The skills proposed in this paper will be

used as the basis for the development of an assessment rubric to measure Systems Thinking Maturity, also sometimes called Systems Literacy (Plate and Monroe, 2010) or simply systems thinking skill. We will derive an assessment system from the skills and rubric, introduced to a set of thinkers, and tested for fidelity. As the proposed skills are described and organized in an assessable way, they are key to the success of the research objective: uncovering effec- tive methods of systems thinking assess- ment, and, ultimately, delivering the vastly important concept of systems thinking to a broader audience.

INTRODUCTION To those outside the systems community,

the term systems thinking may feel complex or far-removed from reality. The truth, however, is quite the opposite. It is important to realize that systems, in this case, refer to all kinds systems. Interpersonal relationships, engineering projects, economies, school systems, organizations; these are all systems, and can all benefit from systems thinking. Systems

thinking provides skills such as the ability to view issues holistically, and the insight to see unobvious connections between things while understanding why they behave a certain way. These skills could apply equally as well to improving a relationship with one’s children as they could to improving pedagogy techniques in impoverished communities (Luong and Arnold 2016). Others claim applying systems thinking can aid us in avoiding the most threatening environmental disasters facing our planet or greatly lessening them(Vallero and Letcher 2013).

A critical step in assessing systems think- ing is to identify the metrics and qualities that thinkers must master to improve their levels of Systems Thinking Maturity. How- ever, systems thinking cannot be broken down to sub-elements, for it is an emergent outcome of the skills that support it. The act of reduction is to defeat its essence as a sys- tem. The mental model for the identifica- tion of systems thinking skills should be to identify skills that support systems thinking ability, rather than the skills that systems thinking is “made up of.” Systems thinking

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is its own system, and there are also skills that support it. Systems thinking cannot be regarded as, simply, the skills that support it. When examining systems thinking as a system by considering both the forest and the trees (Richmond 1993) and seeing both the whole and the parts (Hatfield 2011), it becomes clear that both the individual concepts and systems thinking as a whole are key to its assessment.

A systems thinker must use an under- standing of the way a system’s agents interact to generate a theory of behavior. In the same way, we must use an understanding of the proposed systems thinking concepts, and the way they interact with each other, to assess Systems Thinking Maturity. Ultimately we must take a Systems Approach to measuring Systems Thinking Maturity, and celebrate the similarities between skills rather than the differences (Richmond 1993).

PROBLEM All systems are “made up of stuff, ” and

the way that one organizes and expresses “stuff “ depends on the context in which one will use the “stuff.” When we describe systems thinking, we express its “stuff” in a particular way to facilitate assessment and education. As the first step in this approach, we examined concepts of systems thinking described in the literature. Some of these include wholes and parts, dynamic behavior, conceptual modeling to simplify systems, feedback loops, delays, synergy, multiple perspectives, and uncertainty, among others (Arnold and Wade 2015, Bonnema 2012, Ossimitz 2000, Plate 2010, Richmond 1994, Stave and Hopper 2007, Sweeney and Sterman 2000).

But what must a person do to demon- strate looking at both wholes and parts, or understanding dynamic behavior? The person must determine the practical applications of systems thinking to the real world. From there, one must identify the skills a person must perform to be using systems thinking. Those abilities can then map to the theoretical concepts above. This approach is analogous to determining student learning objectives (SLOs) in the education field, or determining acceptance criteria in the software engineering field.

There is a gray area in which systems thinking skills match up with the theoret- ical concepts; this area is likely to be open to some amount of interpretation. Such limitations are inherent in many fields, es- pecially when taking practical applications and mapping them to educational con- structs (Cuevas, Matveev, and Miller 2010). However, to evaluate quantitatively in an education system, we must take the bot- tom-up approach of defining and mapping the theory to a taxonomy. But, to actually

evaluate realistically relevant skills, we must take the top-down approach of determining the real actions that people take and then mapping those to some form of objectives. The area in which the “rubber meets the road” between these two approaches is likely to remain somewhat ill-defined; however, as long as this research proves that it is possible to apply various methods to evaluate practical systems thinking skills, the research goals are accomplished. Taking a systems approach to a problem reveals that there is no such thing as a complete theory; the quest is to look at a problem more comprehensively, and the resolutions come from rethinking how we deal with complexity (Senge, 1990) and with it, sys- tems thinking.

TWO FACETS OF SYSTEMS THINKING When identifying systems thinking

competencies, it is important to point out that the boundary of the system of sys- tems thinking extends further than simply systems understanding. We consider systems thinking as encompassing two distinct facets, or areas of skill:

■ Gaining Insight: Improving systemic insight of a particular system

■ Using Insight: Applying systemic insight to a particular system.

These are two very different sets of techniques. Systems thinking includes both the ability to gain systemic insight and the ability to use that insight to understand and affect systems. To ignore one of these areas or to fail to recognize their distinction from each other is to invite partial understanding of systems thinking.

Gaining insight roughly equates to ap-

proaching systems from the outside, such as examining a system from multiple perspec- tives. This includes techniques for effectively understanding system behavior even in the face of lacking specific understanding of all the details on how the system works (Wade and Heydari, 2014). What does a person do when she can’t understand all the details of a systems operation, and what are her tech- niques for trying to understand its behavior?

Using insight roughly equates to ap- proaching systems from the inside, such as manipulating system structure. This encompasses the understanding of systems, system structure, and dynamic behavior, all widely considered highly relevant aspects of systems thinking (Hopper & Stave, 2008; Richmond, 1993; Squires, Wade, Dominick, and Gelosh, 2011; Stave and Hopper, 2007; Sterman, 2003).

These two sets of techniques are used both in parallel and in series, constantly reinforcing each other while a thinker explores a system of interest (Figure 1).

SKILLS THAT SUPPORT SYSTEMS THINKING This section proposes a set of skills that

support systems thinking. These skills support the four basic principles of systems thinking as per the Arnold and Wade (2015) definition:

1. Identifying Systems 2. Understanding Systems 3. Predicting System Behavior 4. Devising Modifications to Systems to

Produce Desired Effects. 1

1 Implicit in this 4th principle is also the concept that a systems thinker must determine if a modification has produced the desired result.

Key

While working with a system of interest, thought processes intertwine these two areas which work together to spiral ever closer towards a systemic goal.

Gaining Insight Using Insight

Figure 1. The Systems Thinking Spiral: gaining and using insight

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There are many valid ways to organize and ponder the skills that support systems thinking. This paper proposes dividing the skills into four basic domains. Some models have broken out systems thinking into even more skills. However, in the interest of tak- ing a systems approach and avoiding reduc- tionism, we deliberately synthesized and simplified the skill model for this research using a holistic perspective. In each of the domains, keeping the wholes and parts both in mind, we separated out several sub-skills. The combination of skills in these four categories covers a large majority of the skills and domains where disciplines desire measurements according to the literature and is the most appropriate way to approach skill measurement for this research. The domains and their skills are:

1. Mindset – How to approach systemic problems 1.1 Explore Multiple Perspectives 1.2 Consider the Wholes and Parts 1.3 Effectively Respond to Uncertainty

and Ambiguity 1.4 Consider Issues Appropriately 1.5 Use Mental Modeling and

Abstraction

2. Content – What’s in the system 2.1 Recognize Systems 2.2 Maintain Boundaries 2.3 Differentiate and Quantify

Elements

3. Structure – How’s it organized 3.1 Identify Relationships 3.2 Characterize Relationships 3.3 Identify Feedback Loops 3.4 Characterize Feedback Loops

4. Behavior – What happens when content and structure interact 4.1 Describe Past System Behavior 4.2 Predict Future System Behavior 4.3 Respond to Changes over Time 4.4 Use Leverage Points to Produce

Effects.

A sample line of reasoning along the lines of these domains might be: How do I learn about systems (Mindset)? Does this thing belong in the system (Content)? How is this thing related to other things (Structure)? What’s happening when these things interact, and how can I make it do what I want (Be havior)? Now how do I discover more about this system (Mindset)?

People often associate systems thinking with a variety of cognitive personality traits. Although that line of research is fascinat- ing, this research focuses on the actual construct of systems thinking, not the cognitive traits commonly associated with

successful systems thinkers. This research focuses on identifying and quantifying what systems thinking actually is, rather than the mental traits correlated with its development and use by thinkers (for example, “open-minded-ness”).

MINDSET DOMAIN How Do We Approach Systems and Systemic Problems?

This foundational, yet highest-order set of systems thinking skills is simultaneously a mindset that precedes all other systems work, a philosophical set of principles that accom- pany all systems thinking activities, and a set of paradoxical feedback loops that enable effective systems thinking. This may sound complex, but the key point is that the effective use of these skills results in a mindset, and tends to manifest as problem-solving philos- ophy. The paradoxical nature of some of these principles implies an ability to juggle two opposing facets of a phenomenon and, rather than become confused or frustrated by this opposition, recognize and use the inherent truths of each facet to advantage.

The Mindset skills tend to mature and develop over time as a set of higher order emergent skills which encompass and enhance all systems work. Despite their higher order nature the Mindset skills are probably also the first that should be taught to a systems thinker, and thus they are listed first in this skill set. A thinker may not need insight into a particular system to use these skills; these are Gaining Insight skills and represent some of the special ways that a systems thinker develops and enhances systemic insight. [Skill 1.1]

Approaches a system from only one perspective

Explores other familiar perspectives when approach- ing a system

Begins to explore unfamiliar or contentious perspectives

Actively explores unfamiliar perspectives, but still tends to miss some non-obvious perspectives

Actively explores multiple, non- obvious perspectives, some of which might conflict with the thinker’s view

Skill 1.1 Explore Multiple Perpectives

Low Maturity High Maturity

Does not consider the system holistically

Considers some holistic aspects of systems but misses others; tends to spend too much time in particular areas

Considers the system holistically but tends to miss the importance of the parts; occasionally gets stuck in an event

Tends to consider the system holistically and considers the importance of the parts in most cases

Considers both the “forest” and the “trees” keeping “one eye on each” consistently while approaching systems

Skill 1.2 Consider the Wholes and Parts

Low Maturity High Maturity

Stops when faced with uncertainty or ambiguity

Difficulty making decisions during uncertain times or in ambiguous circumstances

Decisions made when faced with uncertainty are as often flawed as are appropriate

Decisions made when faced with uncertainty are often appropriate

Able to make sus- tainable system decisions despite uncertainties in their outcomes

Skill 1.3 Effectively Respond to Uncertainty and Ambiguity

Low Maturity High Maturity

A systems thinker investigates a problem by objectively examining multiple subjective perspectives (Richmond 1993 Waters and Waters 2014). A thinker needs to look at a problem from many different perspectives and in many different ways. Some of these ways might be non-obvious, unfamiliar, or even distressing, especially if they conflict with a thinker’s world-view. [Skill 1.2]

A systems thinker considers both the “forest and the trees” (Richmond 1994). An appreciation for both the wholes and parts, simultaneously, is a critical systems thinking skill (Richmond 1993, Senge 1990, Stave and Hopper 2007). [Skill 1.3]

Initially, it may be difficult to determine the best solution to a systemic problem, if one even exists. When dealing with systems, uncertainty and ambiguity are often present. However, a systems thinker should be able to make decisions that guide a system towards a desired state (Burandt 2011). A systems thinker needs to have the ability to move forward while analyzing or designing a system, despite the uncertainty inherent in any complex system. An ability to effectively respond to this ambiguity without simply stopping work, becoming stuck, or making inappropriate decisions is an important systems thinking skill.

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One way to effectively respond to uncertainty is through successive approximation (Waters and Waters 2014). Using successive approximation, a systems thinker may try a solution and then assess the results in cycles, moving closer to a systemic goal with each successive trial. This skill supports many other skills, such as investigating relationships (especially unknown ones) and the productive inquisition that is core to systems thinking. [Skill 1.4]

Takes a reactionary approach to issues

Takes a reactionary approach to issues, but tends to realize that this approach has flaws

Sometimes takes appropriate time to allow issues and complexities to emerge; still reacts to issues / jumps to conclusions sometimes

Rarely jumps to conclusions when issues occur; often spends appropriate time to absorb complexity

Allows time for the complexity of a situation to sink in; rarely, if ever, jumps to conclusions; almost always considers issues appropriately

Skill 1.4 Consider Issues Appropriately

Low Maturity High Maturity

Does not recognize the value of mental modeling; intuitive models are highly inaccurate, overly simple, or overly complex

Recognizes the benefit of simplification through mental modeling; mental models may be inaccurate, overly simple, or overly complex

Recognizes that different mental models can influence perspectives and actions differently; able to simplify the problem through mental modeling with some accuracy and simplicity

Able to simplify the problem through mental modeling with increasingly accurate results using increasingly simpler models; recognizes that all models are flawed but some are useful

Devises the simplest mental model that accurately describes the system for a given purpose; recognizes that all models are flawed but some are useful

Skill 1.5 Use Mental Modeling and Abstraction

Low Maturity High Maturity

(Boardman, Sauser, John, and Edson 2009, Frank 2012, Valerdi 2012). Identifying the elements within a particular system (its contents) is, in fact, defining its boundary.

Consider system boundaries in the context of quantum physics. Systemic elements have conceptual similarities to electron density in atoms. Elements and relationships in a system can have probabilities of relevance. The closer an element is to the most important components of the system, the higher the probability that the systems thinker should include it in a particular system of interest. Outside of the obvious components lies a large gray area in which the probabilities of relevance fall off drastically, beyond which lies the “rest of the world” – items that exhibit very low probabilities of relevance and thus are not appropriate for inclusion in the boundary of the system. One can think of this concept can as system boundary density. Similar to the idea that electron density is the measure of the probability of an electron being present at a specific location in an atom: system boundary density is the measure of the probability that an element is relevant to a system in a particular context and/or at a particular time.

This concept may be one of the causes of difficulty in defining system boundaries; the boundaries themselves are often ill-defined and not as clear as might be desired. They tend to change as the context and problem- at-hand changes (Wade and Heydari 2014). A skilled systems thinker will recognize the system boundary density of particular elements and pick the appropriate elements out from the gray area for inclusion in the system of interest. An inexperienced systems thinker might extend the gray area too far (including irrelevant or extraneous items) or not far enough (failing to include key elements and interactions).

Understanding how and why systemic boundaries are difficult to define helps to determine how this skill might be demonstrated and evaluated in a research scenario. [Skill 2.1]

Recognizing that a particular problem is systemic is often considered the first step

An experienced systems thinker takes time to absorb the complexity of a situation rather than reacting immediately to (even stressful) stimuli (Waters and Waters 2014). Considering issues appropriately is a key part of the systems thinking mindset. The ability to determine what “appropriate” means for a given system is also part of this skill. [Skill 1.5]

It is not possible to fit all of the reality into our minds; therefore, we model various aspects of reality (Richmond 2004). Our mental models are simplified abstractions of parts of reality used to make meaning out of what we’re experiencing. Systems thinkers mentally model systems and parts of systems as a way to simplify and understand structure and behavior. These models are fluid and constantly updated, and often support the ability to communicate complex systemic nature in simpler, more approachable ways. Systems thinkers also use mental models to create and test assumptions mentally via thought experimentation.

Part of the mental modeling skill is the appreciation for the different types of mental models and how they can affect human behavior in systems (Waters and Waters 2014). For example, two thinkers investigating the same phenomenon but approaching it with two different mental models may arrive at different conclusions. Both sets of conclusions may well be valid and may include useful details excluded in the other. An appreciation for the different

types of models reinforces the Exploring Multiple Perspectives skill.

CONTENT DOMAIN What is the System, What’s Inside It, and What’s Outside It?

A systems thinker performs a variety of activities while resolving systemic problems. these activities begin with the recognition of a behavior of interest and its associated system or systems (International Council On Systems Engineering, 2014). The importance of choosing appropriate boundaries in systems is widely recognized

Does not recognize that a problem is systemic

Recognizes that the problem is systemic but cannot identify it

Recognizes that the problem is systemic and is able to identify associated behavior or system of interest in general terms

Recognizes that the problem is sys- temic and is able to identify associated behaviors or systems of interest increasingly more concrete terms

Recognizes that the problem is systemic and is able to identify associated behaviors or systems in concrete terms

Skill 2.1 Recognize Systems

Low Maturity High Maturity

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when exercising systems thinking (International Council on Systems Engineering 2014). At this point, the thinker has not yet defined the boundaries of the system but recognizes that such a construct exists and may have a conceptual idea of its contents. [Skill 2.2]

Unable to define the boundary of a system

Able to create an initial mental model of the system that contains some relevant elements. May contain extraneous elements or miss key elements

Able to maintain a system boundary that, over time and context, contains most of the relevant elements and minimizes extraneous elements

Able to main- tain a system boundary of the system over time with increasing accuracy

Able to maintain an accurate boundary of the system that correctly changes over time and context with a high degree of quantitative accuracy

Skill 2.2 Maintain Boundaries

Low Maturity High Maturity

Unable to recognize that elements are different

Able to identify and differentiate between stocks and flows, as well as other types of variables and elements

Able to estimate properties of elements, such as the maximum quantity of a stock or the rate of a flow

Able to quantify properties of ele- ments, such as the maximum quantity of a stock or the rate of a flow with increasing accuracy

Able to describe the properties of elements with a high degree of accuracy

Skill 2.3 Differientiate and Quantify Elements

Low Maturity High Maturity

The boundary defines the content of the system. Maintaining that boundary is a key systems thinking skill (Boardman et al. 2009, Frank 2012, Valerdi 2012). Maintain is the key word here, as it indicates that this skill is continuously applied. The boundary is not defined once and then forgotten; rather, it is continuously maintained and updated over time and with changing system contexts. This boundary is maintained as a mental model. [Skill 2.3]

Understanding and differentiating between the elements in a system, such as their properties, types, and natures, are critical to understanding systems (Plate and Monroe 2014, Stave and Hopper 2007). Differentiating types of stocks, flows, and variables as described by Plate and Monroe (2014) and Stave and Hopper (2007) is a part of this skill. In this case, stock refers to any storage or resource pool within the system. Stocks could range from physical, like the amount of water in a bathtub, to abstract, like the trust level in a relationship between two people. Flows are changes to stocks, such as information flows, energy or material flows, or even decision-making flows. However, this skill extends beyond just stocks and flows, to the nature and properties of other elements in the system. For example, these elements and variables could include particles, pressure, and temperature (for ideal gases) or culture and opinions in human systems.

STRUCTURE DOMAIN How is the Content of the System Organized?

Structure is the way that something is organized (Merriam-Webster 2016). It can also be the arrangement of and relations between the parts or elements of something complex (Oxford Dictionary 2016). System structure, therefore, is

connections themselves. While the systems thinker explores these relationships, an understanding of system structure emerges. More complex systems thinking skills build upon the ability to understand relationships and, by extension, system structure.

In many cases, recognizing a relationship between elements reveals additional system content. Structure and content skills are performed together iteratively. Structure skills seek to connect content, while also revealing gaps in content. Content skills reveal gaps in the structure. As the thinker explores connections and the structure reveals itself, the connections that “point into the unknown” show additional parts of the content.

Relationship recognition skills have two distinct dimensions. Identification is the first, and the second is the ability to grasp a relationship’s strength and properties; also known as characterization. There is a difference between seeing relationships, and understanding how they work. The characterization could be qualitative, such as through estimation, or quantitative, as through precise mathematical modeling. Characterization also implies the ability to understand the connection. A thinker could recognize many connections without necessarily understanding them, or under- stand some connections very well while failing to recognize many others. [Skill 3.1]

Recognizing that two parts of a system relate to one another in some way is a basic systems thinking skill (Senge, et al. 1994, Squires et al. 2011, Stave and Hopper 2007). Relationships are often called interconnections, or just connections. A systems thinker demonstrates increasing levels of maturity in this skill by the ability to recognize increasingly non-obvious, more complex and less visible connections. [Skill 3.2]

Characterizing relationships demon- strates an understanding of how two things

the way the system is organized. It is the way that the parts of the system relate to each other. Recognizing and understanding these relationships, often called interconnections, is core to systems thinking (Richmond 1993; Stave and Hopper 2007, Sterman 2003). However, even highly educated adults without systems thinking training tend to lack skill in this ability (Plate and Monroe 2014). Systems thinkers investigate a system by exploring its many connections, parsing out the important from the unimportant while determining the properties of the

Unable to recognize even those relationships that would be considered obvious by novice systems thinkers

Increasing ability to recognize relationships that are distant or complex in space, time, or other factors; larger volume of relationships recognized

Able to recognize the vast majority of relevant relationships, even obscure, meta-physical, non- obvious, or complex ones

Skill 3.1 Identify Relationships

Low Maturity High Maturity

Unable to characterize the strength of a relationship

Unable to charac- terize the strength of a relationship with accuracy or consistency

Able to estimate the strength of a relationship with some consistency

Able to characterize relationships with increasing accuracy

Able to create highly accurate characterizations of relationships

Skill 3.2 Character Relationships

Low Maturity High Maturity

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are related. Characterizing, in this case, is the distinctive nature or features of a relation- ship. Increasing levels of maturity result in an increasingly clear and accurate picture of how a relationship works, what its characteristics are, and how strong it is. [Skill 3.3]

Unable to recognize feedback loops

Increasing ability to recognize non-linear feedback loops (loops that are distant in space, time, or other factors); larger volume of feedback loops recognized

Able to recognize the vast majority of relevant feedback loops

Skill 3.3 Identify Feedback Loops

Low Maturity High Maturity

Unable to characterize the strength and properties of a feedback loops

Unable to characterize feedback loops with accuracy or consistency

Able to estimate the strength and properties of feedback loops with some consistency

Able to characterize feedback loops with increasing accuracy

Able to create highly accurate characterizations of feedback loops

Skill 3.4 Characterize Feedback Loops

Low Maturity High Maturity

as a combination of all of the Content and Structure skills. [Skill 4.2]

Predicting future behavior is often more difficult than describing past behavior. As with the Describe Past System Behavior skill, the Predict Future System Behavior skill emerges as a combination of all Content and Structure skills. However, future behav- ior prediction also requires an appreciation for the way systems change over time and the way dynamic behavior manifests itself. This includes an ability to recognize epochs of operation after which a system might change in substantial ways. [Skill 4.3]

A key systems thinking skill is the ability to effectively respond to changes in a system over time (Waters and Waters 2014), rather than treating a system as an unchanging entity. If the thinker discov- ers an effective strategy, it can be easy to continue to apply the same strategy to a system repeatedly. However, systems can change in significant, strategy-breaking ways. A systems thinker needs to continu- ously evaluate whether a given strategy is still valid, or whether system behavior is fundamentally different due to changes that occurred over time.

Fundamentally, this skill is about the abil- ity to re-evaluate one’s strategy without fall- ing into a “comfort zone.” A thinker skilled in the Content and Structure domains may indeed devise an effective system-handling strategy at a given time, but does the thinker also possess the wisdom to re-evaluate the strategy when it becomes obsolete?

Relationships can form feedback loops. Although similar, and possibly an extension of the identification of relationships, the identification of feedback loops likely requires additional systems skill. This skill is potentially different than just recognizing that relationships exist or recognizing their strengths; this is recognizing that something different occurred or is occurring here; something emergent. [Skill 3.4]

We must characterize feedback loops regarding their strengths and properties (reinforcing vs. balancing, as well as delays and other temporal properties). As with interconnections, the characterization, in this case, does not necessarily imply a precise quantity. The characterization may start as a highly qualitative estimate of the various features and strengths of a feedback loop but will become more precise as a thinker’s Systems Thinking Maturity increases. As with relationships, it may be possible to characterize several feedback loops with a high degree of accuracy, but yet fail to detect a large number of the relevant feedback loops present in a system.

Characterizing feedback loops also involves the ability to recognize and understand delays. Recognizing and understanding delays is an important system skill (Sweeney and Sterman 2000), and the ability to understand and quantify them may be an indicator of Systems Thinking Maturity (Ossimitz 2002).

BEHAVIOR DOMAIN How do the Organization, Elements, Their Properties, and Other Factors Interact to Produce Behavior? What Can We do to Change that Behavior?

Interconnections, the way they combine into feedback loops, and the way these feedback loops influence and consist of stocks, flows, and variables create dynamic behavior within a system (Arnold and Wade 2015). This behavior can be difficult to grasp without systems training (Plate and Monroe 2014). However, an understanding of dynamic behavior is a key systems thinking skill (Stave and Hopper 2007, Sweeney and

Unable to describe past behavior

Able to describe past system behavior in general, conceptual terms

Able to describe past system behavior through estimation

Able to describe past system behavior with increasing levels of accuracy

Able to describe past system behavior with a high degree of accuracy

Skill 4.1 Describe Past System Behavior

Low Maturity High Maturity

Unable to predict future behavior

Able to predict future system behavior in gen- eral, conceptual terms over short timescales

Able to predict future system behavior in estimated terms over short timescales

Able to predict future system behavior with increasing levels of accuracy over longer timescales

Able to predict future behavior with a high degree of accuracy over a long timescale

Skill 4.2 Predict Future System Behavior

Low Maturity High Maturity

Does not respond differently to changes in the system over time

Recognizes the need to respond differently over time

Responds to changes over time in ways that are occasionally effective

Responds to changes over time in increasingly effective ways

Consistently responds to changes over time in highly effective ways

Skill 4.3 Respond to Changes Over Time

Low Maturity High Maturity

Sterman 2000). [Skill 4.1] Describing past system behavior requires

an understanding of how the system has worked in the past. This ties to the Arnold and Wade (2015) definition of systems thinking, in which understanding and describing system behavior is critical. Past system behavior refers not only to holistic system behavior but also to behavior of specific parts of the system at specific points in time. This skill emerges

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Table 1. Systems Thinking Skill Mapping

# Arnold & Wade Skill/Domain Waters Foundation Stave and Hopper Plate and Monroe Meadows

1 Mindset Paradigms, Tran- scending Paradigms

1.1 Explore Multiple Perspectives Changes Perspective

1.2 Consider the Wholes and Parts Big Picture

Understanding Systems at Different

Scales

1.3 Effectively Respond to Uncertainty and

Ambiguity

Successive Approximation Testing Policies

1.4 Consider Issues Appropriately Considers Issues Fully

1.5 Use Mental Modeling and Abstraction

Assumptions, Mental Models

Using Conceptual Models

2 Content

2.1 Recognize Systems

2.2 Maintain Boundaries

2.3 Differentiate and Quantify Elements Accumulations Differentiating Types

of Variables and Flows Differentiating Types

of Variables and Flows Numbers, Buffers

3 Structure System’s Structure, Leverage

Stock-and-Flow Structures, Rules, Par- adigms, Transcending

Paradigms

3.1 Identify Relationships Connections Recognizing Interconnections

Recognizing Interconnections

Information Flows, Goals

3.2 Characterize Relationships Interdependencies Recognizing

Interconnections Recognizing

Interconnections Information Flows,

Goals

3.3 Identify Feedback Loops

Connections, Consequences, Time

Delays Identifying Feedback Identifying Feedback

Delays, Balancing Feedback Loops,

Reinforcing Feedback Loops

3.4 Characterize Feedback Loops

Interdependencies, Consequences, Time

Delays Identifying Feedback Identifying Feedback

Delays, Balancing Feedback Loops,

Reinforcing Feedback Loops

4 Behavior Understanding Dynamic Behavior

Understanding Dynamic Behavior Self-Organization

4.1 Describe Past System Behavior

Creating Simulation Models

4.2 Predict Future System Behavior

Creating Simulation Models

4.3 Respond to Changes over Time Change Over Time

4.4 Use Leverage Points to Produce Effects Leverage Incorporating Systems

Thinking into Policies Use of Leverage Points

SKILL MAPPING Are These the Skills that the Systems Community Is Looking For?

Table 1 maps the proposed set of systems thinking skills to several of the more prominent systems thinking skill sets as a way to validate the coverage of the skills. The sets chosen are from the Waters Foundation (2014), Stave and Hopper (2007), Plate and Monroe (2014), and Meadows (2008). One should note that the Meadows Leverage Points are not intended to be systems

thinking skills; they are ways to influence systemic outcomes (Meadows 2008). However, it is still important to ensure that they are covered by the proposed skill set. It is also important to note that we mapped the validation skills in the table using a “best- fit” strategy; we placed them in the most appropriate one or two (rather than the only one or two) relevant Arnold and Wade skills and/or domains.

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The Waters Foundation systems thinking habits focus on the way a person thinks and acts when interacting with systems. This focus becomes clear in the way that the Waters skills fill out the Mindset domain. The Structure domain also maps well to the Waters skills. This mapping shows that an understanding of the way systems are organized to influence the way a thinker acts when interacting with systems. Actions towards something are affected by perceptions about how that thing works.

Both the Stave and Hopper and Plate and Monroe skill sets tend to map to the Structure and Behavior domains. This is reflective of their systems dynamics roots and shows their emphasis on the Using Insight part of the Systems Thinking Spiral.

The Meadows leverage points, in general, tend to be ways of making systems produce desired outcomes (Meadows 2008). Therefore, the leverage points do not map to the Mindset skills, and only partly to the Content skills. Most of the leverage points are ways to change the structure to influence system behavior. Intuitively, they are a good fit for the Structure domain.

Table 1 shows a common thread between all of the skill sets – a high concentration of skills around the Structure domain. Every skill set has at least one skill that maps to every Arnold and Wade Structure skill. As the original creator of the “systems think- ing” term once said, “it’s all about structure” (Richmond 1999).

WHY A NEW SKILL SET Key questions drove the development of

the set of systems thinking skills described in this paper:

■ What core principles are universally similar between systems thinking skills described by different authors and experts?

■ What are the different ways that these skills might work together as a system?

■ How can the skills be described in a way that is measurable, accessible, and understandable?

■ What are the interrelationships among these skills?

■ Which of these skills are relevant under which contexts?

■ How do these skills relate to the Arnold and Wade (2015) systems thinking definition?

■ How can the many different facets of systems thinking be captured in a single skill set?

The proposed skill sets is an important step beyond the skill sets used for validation and others that have come before. The Arnold and Wade set is based upon a comprehensive definition of systems thinking published by Arnold and Wade (2015). It appears to be the first published definition that explicitly describes systems thinking as a system. Universally, the previous skill sets have been based upon definitions of systems thinking that have not done this. Ideally, the skills should derive from a systemic definition as they should be examined as a system to fully understand how they work.

Table 1 shows an interesting phenomenon: although the proposed skill set covers all skills in the comparative skill sets, the inverse is not true. Each of the skill sets that came before is, in fact, a sub- set of the Arnold and Wade skill set. Certain important skills, such as Maintain Boundaries, seem to be missing from the other sets. Also, some of the previous skill sets tend to emphasize the Using Insight part of the Systems Thinking Spiral (Stave and Hopper, Plate and Monroe, Sweeney and Sterman, Ossimitz) while others tend to emphasize the Gaining Insight part (Waters Foundation). This research seeks to assess both of those aspects of systems thinking, and thus requires a skill set that emphasizes both, along with their interactive and systemic natures.

We organized the proposed skill set in a way that can be assessed. Certain aspects of the previous skill sets are difficult to assess quantitatively. As one of several examples, assessing the skill of “Understanding Dynamic Behavior” as stated by Stave and Hopper (2007), seems difficult. How is that skill demonstrated? The Arnold and Wade skill set provides the skills Describe Past Behavior and Describe Future Behavior, which one assesses by, simply, asking a thinker to do those things. We removed terminology such as “understanding” from the Arnold and Wade set and replaced with action verbs, as per current educational assessment guidelines.

The Arnold and Wade skill set is accessible outside the systems community. It organizes the skills into 4 simple and clear domains, then attempts to name and describe the skills in each domain using approachable terminology. Plate and Monroe took important steps towards clarification of their skills using accessible language (Plate and Monroe 2010), and the Waters Foundation describes their skills

in an approachable way; however, many of the skill names and contents remained esoteric. For example, the concepts of “Understanding Dynamic Behavior” or “Testing Policies” expressed in some previous systems thinking skill sets may not be intuitive to a mainstream educator lacking formal systems training. Such educators are precisely the people that we need to pick up the systems thinking banner and carry it into mainstream school systems. Unfortunately, systems are complex and describing a systems thinking skill set verbally is not a simple ask. However, if the goal is to push systems thinking across community barriers to other fields, then approachable language is on the front lines.

CONCLUSION This paper presented a complete set

of systems thinking skills and maturity levels to apply widely to many different disciplines. These form a logical, sequential set of skills for use both in instruction and as a means of assessing one’s system thinking capabilities. We derived this set of skills from a review of the literature, the application of systems thinking experience, and the application of systems thinking to itself.

Two facets of systems thinking, Gaining Insight and Using Insight, were described are ways to differentiate two types of systems thinking activity. Of the four skill domains in the proposed skill set, the systems thinker tends to use Mindset skills while gaining insight. The systems thinker tends to exercise Content, Structure, and Behavior skills while using insight. However, these boundaries are not meant to be confining. The systems thinker may use all skills while both gaining and using insight while interacting with a system of interest.

The proposed skill descriptions will be the basis of future work in the development of simulations that we will use to assess one’s systems thinking skills and capabilities automatically. It is likely that there will be a continued evolution of this skill set as these simulations assess the systems thinking capabilities of novice and expert systems thinkers. 

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ABOUT THE AUTHORS Mr. Ross David Arnold currently serves as the senior research

engineer for cooperative defense programs between the United States and Japan, where his work focuses on algorithm research, distributed systems, and emergent intelligence. He holds a BS in computer science from Rutgers University and an MS in software engineering from Stevens Institute of Technology. He is currently a PhD candidate in systems engineering at Stevens, where his research focuses on systems thinking and related concepts.

Dr. Jon Wade is a professor in the School of Systems and Enterprises at the Stevens Institute of Technology. He currently serves as the director of the Systems and Software Engineering Division, as the chief technology officer of the Systems Engineering Research Center (SERC) UARC and as the INCOSE associate director for academic research. Dr. Wade’s research interests include complex systems, future directions in systems engineering research, and the use of technology in systems engineering and STEM education. Dr. Wade received his EE BS, MS, and PhD degrees in electrical engineering and computer science from the Massachusetts Institute of Technology.

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