Structures of logic in policy and theory:
Identifying sub-systemic bricks for investigating, building, and understanding conceptual
Foundations of Science – anticipated publication date - 2014?
I am indebted to three anonymous reviewers whose suggestions have resulted in a stronger and
more understandable paper. The remaining weaknesses are my own.
Steven E. Wallis, PhD
This paper is based, in part, on a presentation at the 56th annual meeting of the International
Society for Systems Sciences (ISSS). July 15-22, 2012, at San Jose State University, California
Steven E. Wallis earned his PhD in 2006 at Fielding Graduate University, focusing on the
rigorous analysis and integration of conceptual systems. He has a decade of experience as a
facilitator and organizational development consultant in Northern California. At Capella
University, Steve mentors doctoral candidates in Industrial/Organizational Psychology. As
Director for the Foundation for the Advancement of Social Theory (FAST) he supports emerging
scholars working to identify rigorous paths for improving theory, policy, and strategic
knowledge. An interdisciplinary thinker, his academic publications cover a range of fields
including ethics, management, organizational change, and policy. His recent book Avoiding
Policy Failure shows how a systems view of policy models can be used to estimate the
effectiveness of policies before implementation as well as improving policies for reducing cost
and improving results. Recently, Dr. Wallis was appointed to the Fulbright Specialist Roster. The
Fulbright Specialist program supports US Scholars in projects to help improve the capacity of
academic institutions outside the US.
A rapidly growing body of scholarship shows that we can gain new insights into theories and
policies by understanding and increasing their systemic structure. This paper will present an
overview of this expanding field and discuss how concepts of structure are being applied in a
variety of contexts to support collaboration, decision making, learning, prediction, and results.
Next, it will delve into the underlying structures of logic that may be found within those theories
and policies. Here, we will go beyond Toulmin’s logics of claim and proof that have not proven
useful for advancing the social sciences and focus on five structures of “causal logic.” The
results suggest a useful and more comprehensive approach to developing deeper understanding
of our conceptual systems such as theory and policy.
Conceptual System, Theory, Theory Building, Metatheory, Policy, Metapolicy, Causal Logic,
Structures of Logic
Through a process of interaction, our philosophical understandings are changing. From those
changes, emerge new insights to support our ability to understand our world and take effective
action. In the 19th Century, Logic and the Philosophy of science were deeply intertwined. In the
20th Century, they had separated. By the 21st Century, with the triumph of positivism, the overlap
in conversation between the two fields focused on scientific activity more than the logics within
the theories. The discussion around logics, however, is still very much alive – especially around
Toulmin’s logics and processes of argumentation (van Benthem 2012).
Briefly, Toulmin’s framework for logical argument is well known (over 7,000 cites according to
a recent search on Google Scholar™). His process includes a Claim with supporting Evidence, a
Warrant that describes the relationship between the Evidence and the Claim. If needed, Backing
is added to further support the Warrant. For each argument, there may also be a Rebuttal which
may include its own Counter Claim, with Evidence, Warrant, and Backing (Toulmin 2003/1958).
While this has become something of a standard for evaluating argumentation, this approach does
not seem to have provided the tools needed by the social sciences to develop theories and
policies that are highly useful in practical application (Wallis 2012b, under submission, 2011).
Scholars in the social sciences continue to develop theories, conduct experiments, and argue for
the validity of their theories – all without visible progress. Similarly, in the field of policy
analysis, existing methods such as “policy soup” (Kingdon 1997) and empirical methods of
policy evaluation (Schmidt et al. 1979) do not seem to be working, prompting calls for new
approaches (Sabatier 1999). Triangulation is suggested (Roe 1998) along with full spectrum
analysis (Mathieson 2004). These methods include a range of techniques (Wallis 2010c) with
their various strengths and weaknesses. However, they have not yet proved useful so the
opportunity and need exists for new approaches – including approaches based on logics.
But where are the alternatives to Toulmin’s approach of building theoretical understandings
based on deciding between conflicting truth claims? One option is to supplement the focus on
data by looking at the structure of theories (Dubin 1978; Kaplan 1964) and causal relationships
between concepts within theories (Stinchcombe 1987). In reframing Popper’s three worlds Wallis
(2008) identified the need to understand how theory should be evaluated based on its usefulness,
empirical data, and conceptual structure. Subsequent research supported that insight (Wallis
2010a, 2011) in analyses of theories from physics and policy models.
The present paper builds on those insights to investigate and better understand structures of logic
and how they may be used to provide an alternative understanding of conceptual systems such as
theories and policies. And, in that investigation, suggest new directions for improving conceptual
systems to make them more useful. In that investigation, this paper identifies research in multiple
disciplines that seem to support this perspective of structural logics. Intriguingly, that research
across a spectrum of fields suggests an emerging field of scholarship – one with great potential
for advancing the social sciences and improving the human condition.
The focus here will be on the social sciences because they have the most to gain from an
improved approach to building conceptual systems. In addition, it should be noted that the
insights developed here seem to be more broadly applicable across all sciences.
The following conversation is significant in three ways. First, it presents a set of logical
structures that seem foundational to the way we think. Second, it shows how causal logics are
useful for creating theories and policies to support decision making. Third, by understanding
these forms of logic, we can learn to objectively measure them. And, that ability to measure, in
turn, gives us a new way to evaluate theory and so choose our theories for understanding and
engaging the world.
This emerging study of conceptual systems includes an interweaving of perspectives from
psychology (particularly within the study of personalities and decision making), complexity
theory, systems perspectives, and philosophy, including “the coherent, and mutually dependent
presence of the clustering beliefs in the mind of that believer is thus essential to the justification”
(BonJour and Sosa 2003, p. 209). Thus, when we understand the concepts and the rules for
relating those concepts to one another (as in understanding words and the rules for grammar) we
understand the interrelationships more effectively (Quine 1969). System thinking (e.g. P. Senge
et al. 1994; Peter Senge 1990) is considered to be an important discipline for understanding
individuals, teams, and organizations. For example, surfacing mental models and understanding
reinforcing loops. Those same insights that we apply to understanding organizational systems
can also be applied to understanding conceptual systems such as theories and policies.
In the present paper, we will briefly review a growing body of scholarship which shows
emerging approaches to understanding conceptual systems (such as mental models, theories, and
policies) by understanding systemic structure. In this paper, there will be a presentation and
discussion about how concepts of structure are being applied in a variety of contexts to support
collaboration, decision making, learning, prediction, and results. Next, we will delve into the
underlying structures of logic that are found within those theories and policies to identify
structures of logic. The insights generated here suggest a more useful and more comprehensive
approach to understanding conceptual systems such as theory and policy.
LOOKING OUT ACROSS THE FIELDS
In this section, we will view a range of methods that are being applied in various fields to
understand, develop, and apply conceptual systems (mental models, theories, and policies). For
each, there will be a brief description of the methodology, some strengths and weaknesses, and
comparison between them. This review is meant to be illustrative rather than comprehensive. The
general idea here, is to orient the reader to the growing number of approaches as well as their
differences and some similarities.
Across the breadth of the academic world, one of our most revered customs is the literature
review. In dissertations, books, and articles for refereed journals the ‘standard’ calls for a review
of the literature and the presentation of some derived theory that serves as a conceptual starting
point for an academic investigation. While this kind of investigation may lead to some interesting
knowledge and insight, the literature review typically consists of a great deal of “cherry picking”
where the author pulls together a set of concepts in a non-systemic fashion (Wallis 2012a). The
literature review is generally useful for demonstrating the author’s knowledge of the field and
providing some context for the reader. While the method can certainly support the creation of
new theories that are testable and applicable, this tradition has not resulted in the creation of
theories in the social sciences that have been proven to be highly useful in practical application.
Consortial benchmarking (Schiele and Krummaker 2010) is an emerging approach that counters
the limitations of a purely academic approach by including stakeholders from industry. Briefly,
this process uses scholars to provide a form of literature review, then engages industry
participants to interview one-another to determine best practices. The resulting blend of
academic theory and practical insights is held to be an improvement over either one alone. Of
course, there is also the difficulty that there is no way to be certain that industry practices are as
good as they might be. Or, from another perspective, this approach may reinforce the status quo
without necessarily pointing towards improvements in theory or practice. While some theory
may be supported by hard data, there is no method inherent to consortial benchmarking for
evaluating those theories. It is therefore a “soft” process that relies on conversations among
stakeholders to generate insights.
Another soft approach is to conduct a large-scale and wide-ranging literature review that includes
a more structured comparison of the conceptual components. Greenhalgh et al. (2005) present a
meta-narrative approach to analyzing research streams across a broad spectrum of disciplines. In
their metastudy of “diffusion of innovation” their unit of analysis was the “storyline” from each
discipline (including sociology, psychology, epidemiology, etc.). The authors deconstructed, then
re-integrated the storylines to identify similarities and contradictions in patterns of research and
conclusions. Those contradictions suggested new insights. Although the study was based (at least
in part) on empirical studies, their results were not tested in application to obtain specific
Moving toward approaches with more structure, Axelrod’s (1976) ground breaking book
Structure of Decision showed how to map the cognitive structure of leaders engaged in complex
decision making. Based on analyses of the writing and speeches of the leaders, these methods are
both graphic and mathematical and are linked to the outcomes of the decisions.
This approach is applied within fields of cultural psychology, decision making and political
Basing their work on an understanding of Bayes nets theory, Sloman and Hagmayer (2006)
investigate the importance of causal relationships for decision making. Their article provides a
clear and accessible resource in the study of causality to better understand decision making. They
note how “studies of learning, attribution, explanation, reasoning, judgment and decision making
all suggest that people are highly sensitive to causal structure.” (p. 408). That is to say, people
seem to understand the world through causal models. So, a better understanding of our causal
models will enable us to make better decisions. Their insights provide a useful link between
rationality, causality, probability, and the benefits of effective decision making in a variety of
fields including strategic planning, and therapeutic interventions.
Nechval, Nechval, Purgailis and Rozevskis (2010) study Bayesian networks, particularly for
computer modeling, to determine which set of data may be preferable. They also note that the
use of Akaike information criteria (AIC) and Bayesian information criteria (BIC) are effective
methods for evaluating the validity of the model. AIC measures the quality of a statistical model
based on the fit between the data and the model. BIC is a similar measure but contains more
“bias” toward smaller models (with fewer parameters).
Both approaches recognize that a model with more parameters is more likely to account for more
data. However, such approaches work under the general assumption that smaller models are
preferable and that more data is preferable (Kelly 2007). While that may be convenient for the
modeler, such methods of evaluating the potential usefulness of the model do not pay sufficient
attention to the systemicity of the model. And, as a result, the causal relationship between model
parameters may flip when more data is obtained (Kelley and Mayo-Wilson 2012). For example,
where one study shows that changes in A cause changes in B, another study may show that
changes in B cause changes in A. This issue is alleviated by studies that are focused more on the
structure (e.g. Greenhalgh et al. 2005).
Raphael (1982) uses a form of “paragraph completion test” to study the rhetoric of the US and
the USSR through a series of crises. Generally, the paragraph completion approach involves
measuring integration and differentiation within and between sentences of a paragraph on a scale
of one to seven. Measurements are, “inferred from references to trade-offs between alternatives,
a synthesis between them, a references to a higher order concept that subsumes them, and the
like.” (Suedfeld et al. 1992, p. 393). Raphael found that the rhetoric became simpler as the two
sides moved towards crisis. Then, as the sides explored options, their rhetoric became more
complex until a successful resolution was reached. This kind of study suggests the ability to
forecast the onset of crises and potential for conflict.
Suedfeld and Rank (1976) investigate the decision making of successful revolutionary leaders.
Working at the intersection of psychology and politics, they used a paragraph completion test to
score archival material such as speeches and personal correspondence. Findings showed that the
thinking of leaders during military conflict exhibited a relatively simple level of structure. After
the revolution, those leaders who were successful politicians exhibited a notable increase in the
complexity of their thinking. Leaders who were not able to adjust their thinking to the
complexities of political life, tended to have unpleasantly short careers. While this study begins
in the field of social psychology, the authors suggest that studies of conceptual complexity could
draw data from most or all fields of study.
If the structure of texts reflects the thinking of large organizations such as nations, it might also
be used to reflect the thinking of large corporations. And, if published texts can serve as a
reflection of that thinking, it seems reasonable to consider that organizations could work to
surface their thinking – and discuss how the structure of that thinking supports or limits the
ability of the organization to enact successful strategies. That is the approach applied in
workshops to develop causal maps and use those maps to facilitate dialog among the participants
in support of scenario planning (Goodier et al. 2010). With this approach, workshop participants
are facilitated through a process of surfacing causal maps. Comparing those maps, and
identifying useful links between them, helps participating organizations to achieve greater
Moving from the organizational level to the level of the individual and the group, a similar
approach is found in “integrative complexity.” This was used to investigate how students
understood the concepts presented over the course of a semester. Those students with a better
understanding of systemic relationships between the concepts also scored higher on their papers
than students whose understanding was not so systemic (Curseu et al. 2010).
Moving from the personal level to the more conceptual level, Rogers (2008) draws on
complexity theory to evaluate the process of evaluating programs. Where, “Most approaches to
building logic models [for program evaluation] have focused on simple, linear models, but some
have explored how non-linear models might be used.” (p. 30). Nonlinearity, related to chaos
theory, may be seen in systems which include multiple feedback loops (Gleick 1987). While the
interaction of those loops may seem to hold a system (such as an organization) in some sort of
predictable pattern, feedback occurring along other loops may kick the organization into
Rogers’ highly developed approach suggests that the logic model may be used as a tool to
coordinate activities and data reporting for the various agencies involved in a program. Also, for
a model based on causal relationships, it seems that two or more simultaneous causal strands are
required for the intervention to succeed. Or, to put it another way, a simple intervention will not
be useful for changing a complex situation. Her approach is focused on using complex causal
structures and reinforcing loops as guides to support dialog within and between organizations.
Integrative Propositional Analysis (IPA) is a newcomer to the growing family. Based on insights
from complexity theory and systems thinking, this approach is used to quantify the systemicity
and complexity of a variety of conceptual systems including policy (Wallis 2010c), ethics (Wallis
2010b), theories of physics (Wallis 2010a), theories of sociology and how they have changed
over time (Wallis under submission), theories of psychology and how they have changed over
time (Wallis 2012b), social entrepreneurship (Wallis 2009b), learning (Wallis 2009a), and more.
Importantly, as with other methods presented here, IPA has found that more systemic conceptual
systems are likely more useful in practical application (Wallis 2010a, 2011).
The methods presented in this section are similar in that they generally suggest that the more
complex and more structured conceptual systems will be more useful in practical application and
enable a greater opportunity for success. Some of them (e.g. Goodier et al. 2010) are “softer”
because they are mainly used to facilitate conversations that generate useful insights. Other
methods such as integrative complexity (Curseu et al. 2010; Suedfeld et al. 1992) and IPA
(Wallis 2013b) are “harder” because they quantify the complexity and structure of the data
(models, mental models, theories, policies, etc.) under analysis.
Another way to consider these methods is to compare the source of the data. Softer approaches
(e.g. Goodier et al. 2010) use data generated from personal reflection – in the moment of
conversation such as an interview or a workshop. Harder methods (e.g. IPA) rely on published or
archival data – such as speeches, correspondence, and news reports. The most solid data for that
analysis may be found in published academic theories because those theories have passed a
rigorous process of peer review. Some forms of analysis are in the middle because their analyses
require some inference as to the intent of the author. A summary of the methods is presented in
Table 1 – Summary of methods
Method Field (s) Data Source Data Processing Measure (s) Uses
All Existing theories Intuition, Ad hoc /
Business Literature review
and interviews to
learn range of
literature to learn
scale of 1-7
ability to make
Education Students identify
Business Knowledge of
Logic Model Program
relate to the
This collection of methodologies provides a spectrum of opportunity for analyzing conceptual
systems across a wide range of fields and sources of data. With these methods, we can gain a
new view of conceptual systems – as systems. On the softer end of the scale, we can present
conceptual systems graphically in order to more effectively stimulate conversation. On the harder
end of the scale, we can evaluate our conceptual systems more rigorously. In the next section, we
will delve into another layer of detail and a smaller level of scale.
In the previous section, we looked at methods for investigating conceptual systems as whole
systems – a complete house as a structural entity. In order to better understand a house, it is also
important to look at the building materials. After all, you can’t make a good house out of bad
bricks. Just as one cannot make a useful theory without valid data, it is impossible to make good
theory with poor logics.
Because a theory is built of causal propositions, the building blocks of theory may be described
as structures of logic. In this section there will be a discussion around insights into Atomistic,
Linear, Circular, Branching, and Concatenated logics. By developing a better understanding of
the strength of those individual bricks, we can gain a better understanding of our theories and
how they may be made more useful.
Please note that in this presentation we are purposefully avoiding discussions around the validity
of data. This is because quantitative analysis has been thoroughly explored by others. It is a
mainstay of science that needs no elaboration here. So, for the purpose of this paper, we will
work under the assumption that the presented causal relationships are based on some reasonable
level of data. That is to say, sufficient data for a scholar to reasonably believe that (for example),
specific changes in one parameter will cause specific changes in another.
Because we will be discussing structures of logic, it is worth noting here that these are not
Toulmin’s logics. As noted in the introduction, his approach is based on claim and support and
are useful for determining what is true (e.g. Bozeman and Landsbergen 1989). Instead, our logics
are focused on the interrelationships between the concepts within the propositions. To more
clearly differentiate, the logics here may be understood as determining what arrangements of
concepts might be more useful, rather that what is more true (although, clearly, we may expect
an overlap between the two).
Here, it should also be made clear that we are focusing on causality because “causal explanation
is the key to theoretical explanation” (Salmon 1984, p. xi). There are some in the systems
community who (quite rightly) are suspicious of simple causality. They prefer to think in terms
of co-causality, circular causality, feedback loops, and so on. Here, we are agreed. Such complex
interactions are needed for systemic understanding. However, for this section, we are
purposefully delving into the sub-systemic components to gain insight into how they function.
In a sense, each propositional statement may be understood as a primitive (partial) system unto
itself. Although these building blocks, by themselves, are simple, we will later see that a suitable
combinations of logics will lead to theory that is more complex and more systemic. This is
similar to the way physicists investigate sub-atomic particles to gain a better understanding of
how atoms work.
Let us begin with a brief discussion on the proposition. “A proposition is a declarative sentence
expressing a relationship among some terms.” (Van de Ven 2007, p. 117). A valid, or useful
proposition is one that describes how specific changes in one thing causes specific changes in
another. For example, “More collaboration causes more communication” is a proposition that
describes two things, how they will change, and the direction of the causal relationship.
Because those things are present, that seems to be a valid proposition. In contrast, a proposition
that is not specific and/or does not relate to change would not be as useful for building theory.
For example, "All organizations have collaboration and communication” is more of a truth claim
or an axiom than a proposition. It is a sweeping claim that – within the structure of logic – does
not provide information that might be useful for comparing organizations (or communication, or
collaboration). That means it becomes more difficult to effectively validate, disprove, or falsify
based only on the structure of logic, itself.
Instead, if one wished to explore the validity of the claim, one would be forced to go beyond the
very limited boundaries of the logic structure. That means adding assumptions about what
constitutes organizations, communication, and collaboration. While many scholars might
disagree about the meaning of those concepts, we might imagine that their understandings would
change as research progressed. With research, scholars would be likely to identify how those
concepts might be related to one another and to other concepts. For example, they may find that
family-run organizations tend to have more communication. This, as a causal relationship
provides a more clear and so more testable hypothesis.
Each causal proposition has an inherent logical structure reflecting the causal relationship
between two or more things. This kind of relationship supports scholars in their effort to confirm
the validity of the proposition. For example, scholars may conduct experiments and/or test the
proposition in practical application. Therefore, it is important that the logical-casual structure
should have sufficient clarity so that multiple scholars who read the proposition would generally
agree on what the structure of a logical-casual proposition is. This kind of approach will support
multiple forms of validation (Wallis 2008) thus supporting more successful triangulation (Lewis
and Grimes 1999; Roe 1998; Saunders et al. 2003) in theory, policy, research, and practice.
In the present paper, we are developing additional forms of validation based on the structures of
logic. Those forms of logic are presented, in abstract form, in Figure 1.
“is” or “is true” or “is real” or “has importance”
Figure 1 - Five Structures of Logic from (Wallis 2013a)
An Atomistic logic structure is a single concept – like an unsupported truth claim. It is much like
having a proposition that says “A is true” or “A is important” or “A is real.” Indeed, the
Atomistic logic straddles the line between Toulmin’s logics and those presented here. Because
there is nothing explicitly causal within the logic itself, it has difficulty being counted as a valid
proposition according to the above definition.
By themselves, Atomistic claims are not highly useful for improving our understanding. Of
course, other meanings and relationships may be inferred by a reader of atomistic claims.
However, such inferences are not a formal part of the conceptual system. Indeed, formally, it is
not quite a system by itself (although, of course, concepts are necessary components of any
conceptual system). Yet, because of the surrounding cloud of inferences, it is possible to apply
Atomistic logics usefully. For example, if one wants to walk from one room to another, it is
useful to differentiate between a wall and a door. So, it would seem that Atomistic logics are
most useful in very familiar situations – where the context is relatively well understood.
We will take action to support the amalgamation of unions
Figure 2 – Example of an Atomistic logic
The Atomistic logic in Figure 2 is part of the Australian Prices and Income Accords (Wallis
2011). While the concept presented in Figure 2 is only part of the conceptual system, it serves as
an example of an atomistic logic structure. Here, because there are no clear casual connections to
other concepts, we have an unsupported claim. Indeed, the atomistic structure raises a number of
questions. For example: What resources will be applied to this effort? How far will the
amalgamation proceed (will partial amalgamation be sufficient, or will they continue until
complete amalgamation is accomplished)? What will the amalgamation accomplish?
The astute reader will note that it is possible to discern many concepts within this atomistic
structure (e.g. action, support, unions, etc.). It is important to note here that this is considered to
be an Atomistic logic because it was presented as a separate and distinct concept within the
original text. This raises a key point in this approach to looking at logics. Specifically, to
understand logics it is important to recall that we are looking at the relationships between the
concepts – not delving into the concepts themselves. Or, from another perspective, we want to
get away from thinking “inside the box” and start thinking “between the boxes.” This will
become increasingly clear as we look at examples of other logics.
To make an Atomistic logic more useful, it is often supported with other claims. This leads us to
Linear logics. That structure of logic may take the form of a proposition stating, “More A causes
more B and more B causes more C” and so on. Such linear representations of the world are, as
systems thinkers are aware, of limited use.
As with Atomistic logics, Linear logics may be useful situations that are relatively simple or very
familiar. For example, If one were to open a door, walk into the kitchen, choose a menu, and
prepare a meal. That would seem to be fairly straight-forward. For more complex situations,
however, a linear approach may be deceivingly simplistic. For example, Sabatier (1999) has
criticized the policy-making process which is often diagramed as simple, linear, chain of events.
He notes, instead, that the process is much more complex and nonlinear. The resulting policies,
similarly, are often depicted simplistically – ignoring how one cause may lead to more effects
than were anticipated (McLaughlin and Jordan 1999).
1. More self sacrifice
2. More sincerity
3. More moving of opponents toward being supporters
Figure 3 – Example of a Linear logic
The Linear logic in Figure 3 is from an analysis of Gandhi’s ethical structure (Wallis 2010b). Self
sacrifice causes sincerity which causes more changing opponents to supporters. Again, the
individual boxes represent concepts that appear to be singular as expressed by the original
author. While it is tempting to look within the boxes to deconstruct and explain (or argue) around
the individual concepts, the key to understanding a Linear logic is to see that one concept is
causal to the next. Two concepts would be the minimum. There does not seem to be a maximum
number (unless, somehow, we run out of concepts).
A Circular logic is where a change in any concept will lead back to itself. For example, the
Circular logic in Figure 1 shows that more A will cause more B, which will cause more C, and
that will cause more A. The miss-use of Circular logic is generally frowned upon (e.g.
tautologies). In contrast, however, we often rely on seemingly Circular structures such as
Shewhart’s “Plan, Do, Check, Act” cycle and related circular models for organizational change
(Rothwell et al. 1995).
1. More interaction within group
3. More group cohesiveness
2. More Uniformity of group opinion
Figure 4 – Example of a Circular logic
The example in Figure 4 is of a Circular logic structure (March and Simon 1993) shows that
interaction causes more uniformity of opinion which causes more cohesiveness and back to
cause more interaction. The circular structure leaves other questions unanswered. For example, is
there a maximum level of interaction? If so, it is not accounted for in the circular structure.
Again, what makes this Circular is not the concepts within the boxes, rather it is that one box
leads back to itself. As with the linear logics, there seems to be a minimum of two concepts
(where each may be causal to the other) but no maximum number of concepts.
It is true that many systems thinkers are fond of such loops. So let me make clear that my intent
here is not to suggest that such loops are without use. My point here, is to suggest that a simple
loop is misleading in its simplicity. Recall here, that we are looking into building blocks; and,
while each block may be complete within itself, each also has the potential to link with others to
form a larger structure that will be more useful than the component propositions (a house
provides more shelter than a brick). A real-world complex system would include multiple
interlinking loops, not merely one. Similarly, from a chaos theory perspective (e.g. Gleick 1987;
Stacey 1992; Wheatley 1992), we can understand those loops as combining to form paths of
nonlinear feedback. There are always more loops at different levels that will create unanticipated
Moving from Circles to Branches, Figure 1 shows the structure of a proposition stating, “More A
causes more B and more C. That Branching logic (like Atomistic, Linear, and Circular logics)
gives the appearance of making sense. For example, in the famous “Just Say No” speech (Speech
1984) Regan suggested that a single cause (saying no to drugs) would cause multiple amazingly
positive results (more safety, dignity, health, freedom, values, etc.). While that speech sounded
like it made sense, the results did not match the rhetoric.
1. More that organizational structure arises from institutional myth
2. More elaborate displays of confidence on the part of institutions
3. Less inspection and evaluation of institutions
Figure 5 – Example of a Branching logic
The branching structure in Figure 5 is a piece from an analysis of institutional theory (Wallis
2014) and shows that when organizational structure arises from organizational myth, there will
be two resulting events. There will be more elaborate displays of confidence (posturing) and
there will be fewer inspections (e.g. financial audits). Of course, this leaves the concept of
organizational structure from institutional myth as a “causal orphan.” That is to say, we don’t
know (from the diagram) how to create institutional myths and their related organizational
structures. Also, if we try to understand the myth/structure as that which generates more displays
of confidence and fewer inspections, the branching structure does not show what concepts result
from increasing confidence and lack of inspection.
This points to another important idea of this form of analysis – that every part of the conceptual
system should be “on the page.” That is, any person might believe that they know about
organizational structure (box #1), but if that understanding is not made an explicit part of the
model, we have no way of rigorously analyzing the understanding (not, at least, with logics).
The fifth structure, is the Concatenated form of logic. That is where changes in A and changes in
B cause changes in C. Scholars identify the Concatenated logic as foundational to our process of
thinking and understanding.
From a philosophical perspective, the Concatenated logic can be seen in the classic Hegelian
dialectic (e.g. Appelbaum 1988). There, thesis and antithesis lead to synthesis. More related to
complexity theory, the idea of a Concatenated concept is similar to the idea of emergence in that
something new may be seen or understood.
The Concatenated logic may also be seen in the work of Gregory Bateson (1979). Bateson
referred to this approach as “dual description.” The idea here is that any two descriptions were
better than one. Whenever two perspectives are combined, a new (and better) understanding
emerges. For a biological example, one eye cannot discern depth. Two eyes, in contrast, provide
two perspectives, which the brain integrates to create a third perspective, one with the added
dimension of depth.
Rogers (2008) suggests the benefit of a concatenated perspective noting, “A second aspect of
complication is the existence of two or more simultaneous causal strands that are all required in
order for the intervention to succeed.” (Rogers 2008, p. 36). Sometimes, she notes, these strands
are in tension – also suggested by the identification of conflicting storylines (Greenhalgh et al.
3. More exploration (acquisition of new knowledge)
2. More capital investment
1. Less use of existing knowledge
Figure 6 – Example of a Concatenated logic
The example of a concatenated logic structure in Figure 6 is from an analysis of organizational
learning theory (Wallis 2009a). Here, it is shown that reduced use of existing knowledge and
increased investment will cause an increase in the acquisition of new knowledge. In this logic
structure, the acquisition of knowledge may be understood as the emergent concept – the
synthesis of the other two. Operationally, if one desired to increase the acquisition of new
knowledge, there are two causes that must be addressed to achieve that acquisition. And, as one
manipulates those two causes, one may gain a better understanding of the extent to which each
one influences the acquisition.
Here again, however, we have concepts that are causal orphans. So, if we restrict our
understanding to what is explicitly shown within the structures, #3 may be attainable by
manipulating #1 and #2, but the model does not show how to attain changes in those causal
orphans. Recall that we are not looking within the boxes – we are focusing our attention between
the boxes. A concatenated concept (#3 within this Concatenated logic) may have multiple causal
concepts – as long as there is a minimum of two.
In contrast to the suggested benefits of the concatenated structure, Van de Ven (2007) suggests
that concatenated structures are not desirable – hinting that this is because there are many
variables and they are, “seldom generalized to more abstract theoretical propositions” (p. 119).
However, those are not insurmountable issues. Indeed, it has been suggested that theories might
be better when they include more concepts rather than fewer (Meehl 2002; Wallis 2010a). Van de
Ven also suggests that concatenated structures might be understood as a set of bullet points.
However, a bullet point list of concepts would be more like a group of atomistic structures. So, it
is not clear if he has applied the appropriate insight to the correct structure. This highlights the
opportunity to use structures of logic to differentiate between theories.
Of course, each structure of logic contains or includes atomistic sub-structures. And, most
structures include one or more causal relationships. As conceptual systems are created which
include more concepts, more casual connections, and more structures, those theories and policies
will generally tend to become more systemic.
More specifically, as logics become more interconnected, those overlaps will lead to a higher
level of systemic structure and will also tend to lead toward more co-causality including multiple
However, increased systemicity is not guaranteed. For example, if we create a theory with many
atomistic logics, the connections between them are not clear (and certainly not causal).
Therefore, it would be unclear what the systemic interrelationship might be. Of course, having
many concepts provides a rich opportunity to explore and potentially find such connection.
It is an open question as to the extent to which less systemic structures might still be useful.
Preliminary investigations show that structures with more concepts will generally be more useful
– but only if we have the resources to pay attention to all of them (Wallis 2011). Also, theories
with more concepts may be more difficult to use, but they seem to be at least marginally more
useful than some theories with fewer concepts (Wallis 2010a).
It may now be understood that a theory is a larger system – built of a collection of smaller sub-
systemic parts. Where we see higher levels of systemicity, we see greater ability for learning,
decision making development of policy and more useful theory (Wallis 2010a, 2011; Raphael
1982; Curseu et al. 2010; Rogers 2008; Suedfeld and Rank 1976; Suedfeld et al. 1992).
For example, in one study Wallis (2010a) used Propositional Analysis to investigate the
evolution of electrostatic attraction theory from ancient times through modern times. That study
found that ancient theories had a very low level of systemicity. Of course, they were not very
useful in application. Theories developed during the scientific revolution became progressively
more systemic. And, they were also more useful for explaining and predicting electrical
phenomena. By the end of the scientific revolution, Coulomb’s law (the final theory) exhibited a
very high level of systemicity. Naturally, that theory (including concepts of force, distance, and
charge) is highly useful. It enables engineers to design cell phones and other electronic devices.
In short, by understanding the structure of our conceptual systems, we may more easily evaluate
our theories, concepts, and policies and so build and choose the best ones to use in practical
application to achieve success and reach desired goals. Understanding these sub-structures, and
their relationship to larger structures, suggest the benefit of rigorous methods for integration of
multiple theories across multiple disciplines. Because, if we understand the structure of our
building blocks, we can better understand how they may be assembled more effectively. The way
we would build a wall with rectangular bricks is different from the way we would build a wall
with spherical bricks. It is entirely possible that there are additional structures of logic that may
provide additional insights into the evaluation and integration of theories. What they are, or may
be, remains to be seen.
These examples of logic structures indicate how the relative strengths and weaknesses may be
understood. Of course, these are new insights to an emerging science of conceptual systems, so
more investigation into these structures is warranted.
LIMITATIONS & EXTENSIONS
These are emerging ideas and the research is in its early stages. The present paper is primarily a
philosophical exploration. While the insights developed here are based on empirical evidence
from the cited studies, the results could be supported (or, perhaps, refuted) by additional
research. For example, a study might be performed to identify the structures of logic across
multiple theories to link the structure of the theories with the sub-structures of logic and the
usefulness of the theories. Other directions might be explored. New structures might be
suggested, for example, by a study of chaos theory (c.f. Casti 1995).
Additionally, the exploration could be elaborated upon by conducting case studies for each of the
structures of logic presented here. That, more concrete, study might confirm (or contradict) the
more philosophical considerations presented here. Such a study might identify additional
strengths, weaknesses, benefits, or limitations of the present approach.
The sub-structures presented in the present paper are based on insights derived from reading
academic literature and living primarily in North America. It may be that there are other
structures of logic that may be found in other countries and other cultures. When searching for
such constructs, scholars are advised that it is not enough to simply identify a structure. It is of
equal importance to compare the structure with the successful application of the logic in the real
world. Still, a cross-cultural comparison may be of some interest and hint at how pervasive these
structures of logic may be.
For one example, we may look at a description of logics of argumentation from ancient China
and identify a variety of logical structures. Seligman, Liu & Benthem (2011) provide a source of
logics in their analysis of Confucian and Mohist traditions. Their results serve to highlight some
similarities with the logics presented in the previous section. In one presentation they describe a
form of reasoning to support the clear understanding of names. Essentially, the process of
categorization is closely related to having the words fit the facts. In arguments, in short, “the
disagreement must come down to a matter of fact, with the primary example being the
categorization of objects” (p. 13). Because the focus here is the relationship between the concept
(name) and the facts, this may be understood as a form of Atomistic logic. The sub-structure of
Atomistic logic may be extended by a deeper exploration into categorization as a logic process or
For another example, Seligman, et al. (2011) explain that the ancient Chinese perspective states
that the rectification of names causes language that flows, which enables the completion of
affairs, which enables the flourishing of ritual and music, that in turn allow punishments and
penalties to hit their mark, which give people the ability to control hand and foot. This example
shows a Linear structure – with all its limitations. Please recall at this point that we are looking at
the structure – the causal relationships between the concepts – rather than looking inside the
boxes at the concepts.
In their presentation of Chinese philosophy, a Concatenated structure can also be found. Here,
they note how better judgment and better standards and better understanding of the situation
causes more rightness of action. Thus, rightness of action is well understood as a concept that is
Concatenated from three other concepts (judgment, standards, understanding of situation).
From these brief examples, it appears that the same structures of logic presented in the previous
section may also be found in a wide variety of contexts. While they are certainly wide-spread,
more research is needed before we might claim that they are in any way “universal.”
A wide range of questions may be asked in reference to this emerging science of conceptual
systems. Do different fields of study tend toward different structures of logic? What ratio of
concepts to causal connections might point to more useful theories? What overlaps might exist
between the logics presented here and other concepts of “knowledge,” “truth,” or “reality?”
Another direction for research may be found by asking if having a theory with “more logics”
makes that theory “more logical?” This is an important question because we frequently rely on
our sense of “logic” to make sense of a situation. If we can analyze and compare conceptual
systems based on the number and type of their logical sub-structures, we might have the ability
to decide which theory makes the most “sense.” Do theories with longer Linear structures
(containing more concepts) provide a more useful sensemaking tool? Would a Linear structure be
made less useful by the addition of Branching or Concatenated structures? There is a vast range
of questions to be posed… and explored.
An additional insight that may benefit from additional investigation is the similarity between the
structure of a broader conceptual system and the structure of a logical sub-system. That
relationship hints at a fractal similarity between levels of scale. That similarity might be extended
and studied. For example, on another level of scale, Carnap and Nagel support the idea that the
process of intersubjectivity emerges through social interaction (van Benthem 2012). That is to
say, through the systemic interaction of humans and organizations, more perspectives are
surfaced and more insights are gained that may be applied for practical benefit. Similarly,
increased intersubjectivity within conceptual systems will enable that conceptual system to be
more useful. What might be found if we go up a level to (or beyond) an organizational scale or
down to (or below) the level of individual concepts? The field is open and inviting.
Looking at these structures as pieces of some vast puzzle, we get the sense that there is a new
way to look at how those pieces might be fit together. We might imagine that the concepts inside
the boxes are more about the picture – the data – on each piece of the puzzle. The logics – how
they fit together – are more about the edges of the pieces. If we can wrap our minds around that
distinction, there may be new ways of understanding theories and new directions that we might
call “progress” for science.
This paper presents five structures of causal logic that are different from Toulmin’s logics of
evidence, supporting hypotheses, and truth claims. Although, of course, better logics and more
empirical studies are both required for the creation of more useful conceptual systems.
Understanding structure and sub-structure provides a more nuanced view of theory than previous
understandings that (for example) suggest that theories “are” abstractions that are created by
observing events that are more concrete (Van de Ven 2007, p. 112).
For systems thinkers, this may be an interesting article because it shows how systems thinking is
being applied within other disciplines (notably studies of personality and decision making within
psychology and for program evaluation in policy studies). This may also be interesting because it
offers the opportunity to define the study of conceptual systems as a sub-field of the systems
sciences. This new sub-field would be very important because it uses systems insights to develop
better theories; which, in turn, are used to develop better understandings of other systems (e.g.
physical, biological, human).
The multiple methodologies presented briefly here in this view of the fields have interesting
differences in terms of the source of data (individual interviews, facilitated groups, news reports,
academic theories, and official policies). They also have interesting differences in their methods
of analysis (data, causality, complexity, and logical structures). Another difference is in the way
conceptual systems are used. For example, supporting conversations to generate new insight,
deciding between competing policy models, or predicting the usefulness of theories and policies.
A key insight developed in the present paper suggests that the more we combine our building
blocks, the more systemic the theory becomes. And, the more systemic theories will be more
useful in practical application. Although, it is also the case that larger conceptual systems may be
more difficult to use for research and application because of their size. That problem may be
avoided through greater collaboration between scholars.
Whatever the difficulty of application, whatever the means of coordination, the social sciences
have great potential for human good – and it is time we realized that potential.
Because this emerging field relies on conceptual systems that can be identified in textual material
(personal correspondence, publications, theories, policies, speeches, etc.), and because such
material is common across all disciplines, the rigorous qualitative and quantitative analysis of
that textual material makes this emerging field one of great potential for interdisciplinary studies.
Indeed, this emerging field has vast opportunities for working within and between disciplines to
improve the human condition, including:
•Identifying directions for accelerating the advancement of academic theories (Wallis
•Predicting the usefulness of policy (Wallis 2013a).
•Collaborative scenario planning within and between organizations (Goodier et al. 2010).
•Predicting conflict (Raphael 1982).
•Reducing war, promoting freedom and justice (UN 1945; Wallis 2011).
•Improving ability of students to more effectively learn (Curseu et al. 2010).
•Integrating research across multiple fields (Greenhalgh et al. 2005).
•Integrating academic theories (Wallis 2012a).
•Strategic planning and decision making (Sloman and Hagmayer 2006).
•Evaluating the potential effectiveness of organizational interventions (Rogers 2008).
•Helping individuals to become more effective leaders (Suedfeld and Rank 1976).
Because these systemic perspectives are similar across a broad range of scholarly endeavors, the
methods presented here support the integration of scientific fields through intellectual symmetry
and the democratization of science (Carolan 2006). It is important that the process of creating
more systemic relationships between concepts leads to the creation of theories that are more
systemic and therefore more likely to be useful for research and practical application. Using
these tools, young and emerging leaders have the opportunity to better understand their
conceptual systems. This means that they can more easily understand themselves and the world
around them to choose more useful conceptual systems and to achieve more lofty goals with less
This emerging understanding of systemic interrelationships suggests new opportunities for
research. By measuring the complexity, systemicity, abstraction, and structures of logic, we may
be able to create a periodic table of theories and policies based (in part, perhaps) on the number
and type of logic structures within the conceptual system. Such a table may prove useful for
students in learning, practitioners for choosing, and researchers for identifying directions of
research for the creation of new and more valued conceptual systems. Similar to the way that the
periodic table of the elements indicates ways in which elements may (and may not) be combined
to create novel and useful molecules, a periodic table of theories could provide a useful tool
indicating how theories may (and may not) be integrated within and between disciplines of the
social sciences. Such a table would provide a powerful tool for understanding our society and
enabling change on social and organizational levels.
While the focus here has been on the social sciences, some perspectives were drawn from studies
of theories from physics. Thus, it seems reasonable that the insights generated here may be
generalizable to all sciences. Indeed, there may exist an intriguing opportunity for integrating all
sciences beginning with conceptual systems perspective.
Because this is a rather preliminary exploration into the topic, the subject is wide open for new
insight, creative ideas, conversations and investigations around what constitutes a conceptual
system. And, of course, what makes a conceptual system more useful for improving the human
In this paper there is an overview of how conceptual systems are studied across a broad range of
fields including psychology, sociology, policy and more. A better understanding of our
conceptual systems (e.g. mental models, theories, and policies) will enable us to create
conceptual systems that are much more advanced than previous systems. This represents the
opportunity for a dramatic advancement in our “social” technology. The potential benefits are
nearly beyond imagination.
Appelbaum, R. P. (1988). Karl Marx (Vol. 7, Masters of Social Theory). Thousand Oaks,
Axelrod, R. (1976). Structure of decision : The cognitive maps of political elites. Princeton:
Princeton Universtiy Press.
Bateson, G. (1979). Mind in nature: A necessary unity. New York: Dutton.
BonJour, L., & Sosa, E. (2003). Epistemic Justification: Internalism vs. Externalism,
Foundations vs. Virtues (Great Debates in Philosophy). Malden, MA: Blackwell.
Bozeman, B., & Landsbergen, D. (1989). Truth and credibility in sincere policy analysis:
Alternative approaches for the production of policy-relevant knowledge. Evaluation
Review, 13(4), 355-379.
Carolan, M. S. (2006). Science, expertise, and the democratization of the decision making
process. Society and Natural Resources, 19, 661-668.
Casti, J. L. (1995). Complexification: Explaining a Paradoxical World Through the Science of
Surprise. New York: Harper Perennial.
Curseu, P., Schalk, R., & Schruijer, S. (2010). The use of cognitive mapping in eliciting and
evaluating group cognitions. Journal of Applied Social Psychology, 40(5), 1258-1291.
Dubin, R. (1978). Theory building (Revised ed.). New York: The Free Press.
Gleick, J. (1987). Chaos: Making a New Science. New York: Penguin Books.
Goodier, C. I., Austin, S. A., Soetanto, R., & Dainty, A. R. J. (2010). Causal mapping and
scenario building with multiple organisations. Futures, 42(3), 219-229.
Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., Kyriakidou, O., & Peacock, R. (2005).
Storylines of research in diffusion of innovation: A meta-narrative approach to systematic
review. Social Science and Medicine, 61, 417-430.
Kaplan, A. (1964). The conduct of inquiry: Methodology for behavioral science (Chandler
Publications in Anthropology and Sociology). San Francisco: Chandler Publishing
Kelley, K. T., & Mayo-Wilson, C. (2012). Causal conclusions that flip repeatedly and their
justification. http://arxiv.org/ftp/arxiv/papers/1203/1203.3488.pdf. Accessed 8/25/2013.
Kelly, K. T. (2007). Simplicity, truth, and the unending game of science. In S. Bold, B. Löwe, T.
Räsch, & J. v. Benthem (Eds.), Foundations of the Formal Sciences V: Infinite Games
(pp. 368, Studies in Logic: Volume 11). London: College Publications.
Kingdon, J. W. (1997). Agendas, alternatives, and public policies (2ed.). Upper Saddle River,
NJ: Pearson Education.
Lewis, M. W., & Grimes, A. J. (1999). Metatriangulation: Building theory from multiple
paradigms. Academy of Management Review, 24(4), 627-690.
March, J. G., & Simon, H. A. (1993). Organizations (2ed.). Hoboken, NJ: Wiley-Blackwell.
Mathieson, G. (2004). Full spectrum analysis: Practical OR in the face of the human variable.
Emergence: Complexity and Organization, 6(4), 51-57.
McLaughlin, J. A., & Jordan, G. B. (1999). Logic models: A tool for telling your program's
performance story. Evaluation and Program Planning, 22(1), 65-72.
Meehl, P. E. (2002). Cliometric Metatheory II: Criteria scientists use in theory appraisal and why
its is rational to do so. Psychological Reports, 91(2), 339.
Nechval, N. A., Nechval, K. N., Purgailis, M., & Rozevskis, U. (2010). Selection of the best
subset of variables in regression and time series models. In S. E. Wallis (Ed.),
Cybernetics and Systems Theory in Management (pp. 303-320). Hershey, PA: IGI Global.
Quine, W. V. O. (1969). Ontological Relativity and Other Essays. New York: Columbia
Raphael, T. D. (1982). Integrative complexity theory and forecasting international crises: Berlin
1946-1962. The Journal of Conflict Resolution, 26(3).
Roe, E. (1998). Taking Complexity Seriously: Policy Analysis, Ttriangulation and Sustainable
Development. New York: Kluwer Academic.
Rogers, P. J. (2008). Using programme theory to evaluate complicated and complex aspects of
interventions. Evaluation, 14(1), 29.
Rothwell, W. J., Sullivan, R., & McLean, G. N. (Eds.). (1995). Practicing Organization
Development: A Guide for Consultants. San Diego, CA: Pfeiffer & Company.
Sabatier, P. A. (Ed.). (1999). Theories of the policy process (Vol. 1, Theoretical Lenses on Public
Policy). Boulder, Colorado: Westview Press.
Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. New Jersey:
Princeton University Press.
Saunders, C. S., Carte, T. A., Jasperson, J., & Butler, B. S. (2003). Lessons learned from the
trenches of metatriangulation research. [Article]. Communications of AIS, 2003(11), 245-
Schiele, H., & Krummaker, S. (2010). Consortial benchmarking: Applying an innovative
industry-academic collaborative case study approach in systemic management research.
In S. E. Wallis (Ed.), Cybernetics and Systems Theory in management: Tools, Views, and
Advancements (pp. 93-107). Hershey, PA: IGI Global.
Schmidt, R. E., Scanlon, J. W., & Bell, J. B. (1979). Evaluability assessment: Making public
programs work better (Vol. 14, Human Services Monograph Series). Washington, D.C.:
Department of Health, Education, and Welfare - Project Share.
Seligman, J., Liu, F., & van Benthem, J. (2011). Models of reasoning in ancient China. Studies in
Logic, 4(3), 57-81.
Senge, P. (1990). The Fifth Discipline: The Art and Practice of The Learning Organization. New
York: Currency Doubleday.
Senge, P., Kleiner, K., Roberts, S., Ross, R. B., & Smith, B. J. (1994). The Fifth Discipline
Fieldbook: Strategies and Tools for Building a Learning Organization. New York:
Sloman, S. A., & Hagmayer, Y. (2006). The causal psycho-logic of choice. [Opinion]. Trends in
Cognitive Science, 10(9), 407-412.
Speech (1984). Just say no: Words to the nation. Public address by President Ronald Regan and
Stacey, R. D. (1992). Managing the Unknowable: Strategic Boundaries Between Order and
Chaos. San Francisco: Jossey-Bass.
Stinchcombe, A. L. (1987). Constructing social theories. Chicago: University of Chicago Press.
Suedfeld, P., & Rank, A. D. (1976). Revolutionary leaders: Long-term success as a function of
changes in conceptual complexity. Journal of Personality and Social Psychology, 34(2),
Suedfeld, P., Tetlock, P. E., & Streufert, S. (1992). Conceptual/integrative complexity. In C. P.
Smith (Ed.), Handbook of Thematic Content Analysis (pp. 393-400). New York:
Cambridge University Press.
Toulmin, S. E. (2003/1958). The Uses of Argument. New york: Cambridge University Press.
UN (1945). Charter of the United Nations and Statute of the International Court of justice. New
York: United Nations.
van Benthem, J. (2012). The logic of empircal theories revisited. Synthese, 186(3), 775-792,
Van de Ven, A. H. (2007). Engaged scholarship: A guide for organizational and social research.
New York: Oxford University Press.
Wallis, S. E. (2008). Validation of theory: Exploring and reframing Popper's worlds. Integral
Review, 4(2), 71-91.
Wallis, S. E. (2009a). Seeking the robust core of organisational learning theory. International
Journal of Collaborative Enterprise, 1(2), 180-193.
Wallis, S. E. (2009b). Seeking the robust core of social entrepreneurship theory. In J. A.
Goldstein, J. K. Hazy, & J. Silberstang (Eds.), Social Entrepreneurship & Complexity.
Litchfield Park, AZ: ISCE Publishing.
Wallis, S. E. (2010a). The structure of theory and the structure of scientific revolutions: What
constitutes an advance in theory? In S. E. Wallis (Ed.), Cybernetics and systems theory in
management: Views, tools, and advancements (pp. 151-174). Hershey, PA: IGI Global.
Wallis, S. E. (2010b). Towards developing effective ethics for effective behavior. Social
Responsibility Journal, 6(4), 536-550.
Wallis, S. E. (2010c). Towards the development of more robust policy models. Integral Review,
Wallis, S. E. (2011). Avoiding policy failure: A workable approach. Litchfield Park, AZ:
Wallis, S. E. Existing and Emerging Methods for Integrating Theories Within and Between
Disciplines. In 56th annual meeting of the International Society for Systems Sciences
(ISSS), San Jose, California, July 15-22, 2012 2012a (pp. 23)
Wallis, S. E. Theories of psychology: Evolving towards greater effectiveness or wandering, lost
in the jungle, without a guide? In 30th International Congress of Psychology:
Psychology Serving Humanity, Cape Town, South Africa, July 22-27, 2012 2012b
Wallis, S. E. (2013a). How to choose between policy proposals: A simple tool based on systems
thinking and complexity theory. E:CO - Emergence: Complexity & Organization, 15(3),
Wallis, S. E. (2013b). Propositional Analysis for Evaluating Explanations through their
Conceptual Structures. Paper presented at the International Society for Complexity and
Emergence (ISCE) “Modes of Explanation” Paris, France, May 22-24, 2013
Wallis, S. E. (2014). A systems approach to understanding theory: Finding the core, identifying
opportunities for improvement, and integrating fragmented fields. Systems Research and
Behavioral Science, 31(1), 23-31.
Wallis, S. E. (under submission). Are theories of conflict improving? Using propositional
analysis to determine the structure of conflict theories over the course of a century.
availible on request.
Wheatley, M. J. (1992). Leadership and the New Science. San Francisco: Barrett-Koehler.