Feedback, Social Systems, and Design Thinking – A Practical Implication

Studies in System Science (SSS) Volume 2, 2014

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Feedback, Social Systems, and Design Thinking – A Practical Implication John Pourdehnad1, Habib Sedehi2, Inam Ur-Rahman3 1

Associate Director, Ackoff Collaboratory for Advancement of Systems Approaches University of Pennsylvania, U.S.A

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ICT Senior Consultant and Contract Professor, University of Rome, Italy

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MPhil Candidate, Organizational Dynamics, University of Pennsylvania, U.S.A

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Jpourdeh@seas.upenn.edu; 2habibsedehi@gmail.com; 3iur@sas.upenn.edu

Abstract Feedback is a commonly used term in both engineering and social systems. In engineering systems, the concept of feedback is usually tied with control (or regulation); positive or negative feedback is often utilized to regulate the behavior of systems. In social systems, we use feedback for many purposes; regulation is one, interpretation, integration, and differentiation and learning are others. In engineering, feedback is an important part of system design. In social systems, feedback can be used as an important aspect of system design; and this is the concept we are going to explore in this paper. Before getting to the specific example, we’ll briefly explore different types of feedback, their relationship with different systems methodologies, and an overview of the Systems Dynamics methodology that has feedback loops as its building block. This background will help us examine the design thinking considerations we need to take into account when dealing with social systems.

Keywords Feedback; Systems; System Dynamics; Design

Types of Feedback in Different Systems Methodologies Looking at organizations as social systems, whose components are human beings capable of making decisions in choice situations, signifies that feedback plays a key role in decision-making and specifically, in organizational learning. In consequence, sometimes organizations are regarded as systems of decision-making. Therefore, feedback is not only a feature of the social systems to be studied; but also a matter of the way in which our thinking about how those systems are organized. Social systems, at all times, are confronted with multitude of choices, but majority of these choices fall into two categories: descriptive choice (based on what is known and described by the others) and experiential choice (based on the organization’s own experience). It is a well-known fact that “experience is inevitable, learning is not.” For organizational learning to take place, the social system needs to design rigorous undistorted feedback systems.

Separately, Systems Thinking (ST) both as a mindset (paradigm) and the associated methodologies derived from it, is an evolving discipline. The most significant systems methodologies and their principal characteristics are outlined in the following pages. Each of these methodologies can be related to a system paradigm. Therefore, the methodologies could be better understood if the paradigms underpinning them are explained and understood.

The existence of a plurality of paradigms emphasizes that multiple interpretations of reality lead to differentiated beliefs about what is held to be "true". Rather than attempting to view dogmatically one paradigm as holding the only version of "truth", and the others as being "false", we suggest that each paradigm presents its interpretation of the world from a particular perspective that captures aspects ignored by the other paradigms: a more holistic understanding is gained by taking seriously what each paradigm tells us about the world and contrasting the different perspectives gained.

The significance of belief and meaning in paradigms highlights the importance of language and how we talk about the problems we have to face. Each paradigm is associated with particular stories and language. Metaphors play a crucial part in this context: metaphors are figures of speech in which a word or phrase literally denoting one kind of object or idea is used in place of another to suggest a likeness or analogy between them.

6 In the context outlined above, let’s briefly examine the evolution of philosophical foundations (or paradigms) that 1 shaped ST methodologies and influenced the types of feedback dominant in each of these methodologies. It’s interesting to note that evolution of ST methodologies from Hard Systems Thinking to Postmodern Systems Thinking is in line with the evolution of philosophical foundations outlined in Table 1. The ST methodologies, when viewed

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through the lensof modernist and post-modernist philosophical foundations, present an evolving wisdom about the social systems.

TABLE 1

ST METHODOLOGIES AND PHILOSOPHICAL FOUNDATIONS

Depending on the philosophical foundation, a systems paradigm comes from, the dominant form of feedback employed by ST methodology may vary. Different forms of feedback, utilized in ST methodologies, are regulation, interpretation, integration, and differentiation. Table 2 illustrates the types of feedback utilized by different methodologies. It’s not surprising that these dominant forms of feedback match with respective functionalist, interpretive, emancipatory, and post-modern paradigms1.

TABLE 2 DOMINANT ROLES OF FEEDBACK IN DIFFERENT ST METHODOLOGIES

TABLE 3 ST METHODOLOGIES AND MULTIPLE FEEDBACK ROLES (1)

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ST METHODOLOGIES AND MULTIPLE FEEDBACK ROLES (2)

From Table 3 illustrates that multiple forms of feedback are utilized during different parts/stages of the ST methodologies; and some forms of feedback remain dominant throughout the methodology whereas others serve in a supporting role. As practitioners of ST theories and methodologies, we can benefit from the awareness of different feedback approaches utilized in different stages of ST methodologies; and this awareness can help us make informed choices about selecting full/partial methodologies, and associated tools to meet the needs of a given situation. It’s important to appreciate that the role of feedback, in different methodologies, changes due to the change in the nature of the required task, and due to the change in underlying philosophical paradigms.

Table 2, 3, and 4, it also can be seen that different methodologies either focus on the form, or the content of the discourse. Team Syntegrity, for example, tries to achieve the democratic decision making environment through control of the form of conversation tool. Interactive Planning, and Soft Systems Methodology, on the other hand, leave the form of the discourse at the discretion of the practitioner and focus on the content of the discourse to achieve the desired goals. The role of feedback in Postmodern Systems Thinking is to create localized, provisional plans that don’t seek consensus, accept contradiction, and yet share knowledge to continue co-developing the future.

This form of planning is suitable for multi-agency collaboration where seeking consensus is not realistic.

It’s worth noting that one size doesn’t fit all; amd no one methodology is suitable for every problem situation.

Consensus or conflict, strategy or tactics, form or content, organizational structure or purpose, local or global, individual or community, and control or autonomy are the issues that will continue to co-exist, and an ST practitioner will continue to use appropriate tools, including metaphors, and paradigms, to regulate, interpret, integrate, and differentiate plans, and designs to match with the needs of emerging situations. Awareness of the situational need and capability of available methodologies can help the practitioners pick the right tool for the right situation.

Systems Dynamics: A Case in Point System dynamics methodology is designed to address problems in complex systems, where feedback, delays and accumulations interact to explain the unintended consequences of well-intended policies. The emphasis of systems 1 dynamics (SD) thinkers is on the structure of the system. SD thinkers believe that understanding the structure of system can definitely help understanding the behavior of the system; it’s through the knowledge of system structure that various underlying behavior patterns, decision points, and management intervention points can be identified. In order to understand the structure, elements such as system boundary, network of feedback loops (causal loop diagram), rate/flow, and level/stock variables need to be identified. A system dynamics model is a dynamic hypothesis of the problem; and the problem structure analysis is used to explain the behavior of key variables in the system. In selecting the variables, it’s important to include all the interacting components of the system. Variables that don’t impact the system behavior can be excluded.

System structure models built on stock, flow, and feedback loops are essentially integrated cause and effect relationships. Positive and negative feedbacks, delays, and counter intuitive behavior of systems suggest that cause and effect are non-linear and they are separate in time and space. Through an understanding of system structure, utilizing stock, flow, and feedback loop, both organizational learning, and business strategy can be formulated.

Models based on insufficient, or incorrect data can, however, lead to poor judgments.

The role of feedback in SD can be seen either regulative or interpretive, depending on its application. In a typical

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application, if the purpose of SD is to understand the structure of the system in order to design rather than to “predict” the behavior and hence identify the control or intervention points, the role of feedback is merely regulative.

In such applications, the SD expert views the system from the outside, ignores the human actors involved in the system, takes the objective of the system from the client, and uses his/her specialist knowledge to unearth the underlying patterns that govern the behavior of the structure. Based on the intervention points identified through SD modeling, the intervention points are identified with a purpose to govern the system. This application is close to Jay Forrester’s approach towards SD. Another application of SD approach is popularized by Peter Senge. It’s about learning. Through understanding of feedback loops, stocks, and flows, the consequences of the existing policies can better be appreciated and hence double loop learning can be enabled. The role of feedback in SD, from learning perspective, can be seen as interpretive. Generalizing the system behavior in archetypes can, sometimes, limit learning.

We should anyhow also be aware that almost any management problem in real life has dynamic nature and typically includes feedback loops. Actions are taken, results are observed and new decisions are attempted, achieving new results and so on, in which per definition constitutes a “feedback loop”. Feedback loops exist not only between the decision maker’s actions and the system, but also among the various elements within the system. Hence, most dynamic management problems are also “systemic” in nature. The main purpose of system dynamics methodology is to better understand the causes of undesirable dynamics and design new policies to best “govern” them. Managerial understanding, action and control are at the heart of the method. System dynamics thus focuses on dynamic problems of systemic feedback.

Machine and flux metaphors are dominant in SD thinking. Understanding system structure through patterns of feedback loop, and then devising leverage points to control the system reflect the influence of machine metaphor in controlling the system. Feedback delays, counterintuitive behavior, and separation of cause and effect in time and space are reflective of the flux metaphor. Generalizing the system behavior in archetypes can be seen as an effort to predict (and hence control) the system behavior. Prediction and control are typical attributes of the machine metaphor. Learning associated with these dynamic feedback loops point to the influence of flux (and transformation) metaphor.

The U.S. Political System – A Study of Feedback Loops and Time Delays Let’s now consider the U.S. Political system from a system design perspective. Donations are needed to run the political campaigns. More donations mean more advertisement and possibly more votes in the election. More votes typically result in more electoral votes, and hence control of Executive and Legislative power. A party with executive and legislative control can attract even more donations. Clearly, this is a reinforcing loop.

Now in order to satisfy the interest groups, who provide a bulk of the campaign donation, party with the legislative and/or executive control passes (or blocks) the policies and legislation that better serves the needs/desires of the interest groups. More interest-based legislation means more support of the interest groups and more support from interest groups means even more donation for the party. Again, this is a reinforcing loop. Since lobbyists and interest 7, 8 groups work with Congress on daily basis, to pursue the desired, and block the undesired legislation, there’s almost no feedback delay between interest groups and legislators.

This is not the case with the common people who only participate in the elections at defined intervals. They are attracted towards a given political party based on the relevance of campaign slogans (positively or negatively). Once attracted to a given party, the citizens also make campaign donations. Once the party is in power, the citizens can only hope that the politicians they elected will work on their behalf, and solve their problems. Unlike interest groups, there’s no regular feedback mechanism between the legislators and the common people, and it will take years (time delay) for the common people to hear back again from their legislators. Also, since the common people are not organized like interest groups, they have less power to influence the legislative decision making.

Other than time delay, another major difference between lobbyist-legislator and citizen-legislator feedback is the visibility, and transparency of transactions. The lobbyists work with legislators on daily basis; they are less visible to the general public, but more effective in getting their desired objectives. On the other hand, the periodic elections are more visible, but the common people are less effective in getting their desired policies in place.

We can read this situation from the perspective of different types of feedback designed in the social system. The feedback in the lobbyist-legislator loop is more regulative, more frequent, and involved less time delay. The feedback loop in the citizen-legislator loop is interpretative. Citizens interpret the campaign slogans, and make their donation and voting decisions accordingly. The delay time until next interpretive feedback is in years, and lack of regulative

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feedback makes the citizen-legislator loop less effective for the citizens.

FIGURE 1 INFLUENCE OF INTEREST GROUPS IN THE U.S. POLITICAL SYSTEM

Design Considerations Unlike engineering systems, the designer can’t assume the role of an actor sitting outside the system and designing the feedback loops to meet the pre-defined control objectives. In case of a complex social system, like a political system outlined above, the designer can very well be an actor within the system, and the design choices can be limited. Assume that our intent is to introduce design changes in the Citizen-Legislator loop (Figure 1) to make the political process more favorable for common citizens. Knowing that the common citizens won’t have the resources to compete with the lobbyists, and interest groups, we need the design changes that are feasible yet effective. A few options come to mind:  Make the transactions, both financial and legislative, in the Lobbyist-Legislative Loop more visible to the actors in the Citizen-Legislator Loop. Internet can provide a low cost solution.  Reduce the time delay associated with feedback in the Citizen-Legislator loop. Instead of a periodic electiontime feedback, citizens should be able to provide more frequent feedback to their legislator/s concerning the current and/or desired legislation. Again, cyber power can provide a low cost solution, but cyber power alone is not enough. Cyber power needs to be complemented with a better and effective organization of citizens to present their concerns to legislators, timely, and more often.  How can citizens be better organized to actively protect their interests? What are the organizational structures (hierarchy, network, and/or market) that can help achieve these objectives? And how do the civil organizations get plugged into the Citizen-Legislator loop for getting their voice heard is a question that deserves more detail than can be covered in this paper.

Concluding Remarks This paper outlines the possibility of using various types of feedback as considerations in the design of complex social systems. Complex social systems, such as a political system within a nation state, a supranational system covering multiple states (such as EU), or a global corporation can all benefit from a purposeful design utilizing different types of feedback summarized in this paper. It is also important to mention that design, as a culture of inquiry and action, is increasingly touted as an approach to make purposeful change in social systems. Design, as an activity, has been around forever. Proponents of design thinking allude to the fact that almost everything around us, except for nature, has been designed.

Conversely, Systems Thinking is rather new. It was developed in the early 1950s to provide a new mindset and a perspective to better understand the world and tackle it’s ever more complex problems. There is no denial that design in all its manifestation has greatly helped our human societies to make extraordinary progress in every aspect of

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human life. At the same time, many of our societal woes are also the unintended and unacceptable consequences of our design activities.

Today, we are confronted with many "intractable problems" in the world that refuse to go away and are lingering.

These include environmental, economic and political crises that threaten to lower the quality of life for many.

Therefore we suggest that first, there has to be a gradual acknowledgment of the existence of diverse organizational contexts (simple, complicated and complex) that require different approaches to planning, management, leadership and problem solving. Also, there has to be a change in understanding of the role of design and design thinking in facilitating the creation of a desired future. In this regard, we need to create an appreciation that all the men/women are designers and that design per se is not the privilege of a small group of exceptionally creative individuals called designers. This we believe will have a profound impact on the way design is carried out. The participation of multiple stakeholders and, in particular, the consumers in the process of “open innovation” will be further encouraged through democratization of design activities. This will foster a balanced approach to creating a desired future for the social system based on consensus and excitement of the stakeholders.

In this vein, leveraging web technology and social media to call on the wisdom of crowds, known as "crowd sourcing," will also have an enriching effect on the design process and will significantly influence “buy-in” by the participants.

Moreover, we should make a slow but steady progress towards the adoption of systems thinking as an alternative mindset and be mindful of the role of feedback in selection of the appropriate methodology. We know through experience that most individuals can relate to the famous quote by Einstein that says: "We can't solve problems by using the same kind of thinking we used when we created them." The value of systems thinking recognized as a powerful alternative point of view that incorporates feedback and synthetic thinking in addition to the traditional analytical thinking, will afford us with a long view to anticipate how a particular solution will play out over time. It also makes possible the emergence of new designs as a result of the interaction of multiple stakeholders in the design 11 process. Evidence shows that systems’ thinking integrated with design thinking is the appropriate mindset and methodology to cope with highly complex situations.

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Deegan, M. (2007). Exploring U.S. Flood Mitigation Policies: A Feedback View of System Behavior Proceedings of the 25th International Conference of the System Dynamics Society. Boston, MA, The System Dynamics Society.

[10] Barlas, Y. (2002). System Dynamics: Systemic Feedback Modeling for Policy Analysis. Knowledge for Sustainable Development: An Insight into the Encyclopedia of Life Support Systems. Paris and Oxford, UNESCO-EOLSS: 1131-1175.

[11] Pourdehnad J. Wexler E., and Wilson D. (2011) Integrating Systems Thinking and Design Thinking, The Systems Thinker, Pegasus Communications, Volume 22, Number 9.

[12] Pourdehnad J. (2011) The Big Picture – Integrating Systems Thinking with Design, Third International Congress of Systems Sciences, Mexican Academy of the Systems Sciences, Universidad Iberoamericana, Mexico. Retrieved from http://www.slideshare.net/rammarins/jp-presentation-mexico-2011 John Pourdehnad, is Associate Director of Ackoff Collaboratory for Advancement of Systems Approaches at University of Pennsylvania. He is Adjunct Professor of Systems Thinking at the University of Pennsylvania. From 1979, as an associate of late Russell Ackoff, he has worked as a consultant in a broad range of industries spanning several Fortune 100 corporations, non-profit organizations, and government agencies.

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Habib Sedehi is a freelance ICT Senior Consultant and teaches Strategic Management and Entrepreneurship & Business development in Executive MBA courses at Link Campus International University in Rome.He has been full contract Professor of Computer Sciences at Rome University “La Sapienza� where he hold Project Management and System Dynamics Simulation Modeling courses.

Inam Ur-Rahman is an aerospace industry professional with an engineering background and interests in social systems, policy, and development. Inam holds degrees in Electrical Engineering, Industrial and Systems Engineering, and Organizational Dynamics. Currently Inam is pursuing a Masters of Philosophy (MPhil) in Organizational Dynamics at University of Pennsylvania.

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