Thresholds Are Everywhere: A Systems Approach to Public Policy.

AutorCarvalho, Hamilton Coimbra

Introduction

After analyzing recent decades of American foreign policy, Monat and Ganon (2017) concluded that it lacks systems thinking, thus producing a stream of shortsighted policies with unintended, dire consequences, such as the rise of ISIS and other terrorist groups. In the same vein, Morecroft (2015) stresses that event-oriented thinking very often leads to public policies that target symptoms instead of root causes. Such issues are not new. Public policies in different contexts often show the hallmarks of poor design and linear thinking (Dodgson, Hughes, Foster, & Metcalfed, 2011; Lee et al., 2017; Magro & Wilson, 2013; Stroh, 2015). Examples of inefficacious policies include the building of new roads to alleviate traffic congestion, the war on drugs in America, the interventions to tackle the skyrocketing obesity epidemic, and the zero tolerance policies on forest fires (Buchanan, 2000; Ludwig & Rogoff, 2018; Morecroft, 2015; Sterman, 2000).

In fact, notwithstanding systems science's long academic history, there seems to be slow progress towards the incorporation of its major tenets into the repertoire of managers and state agents. Societies as a whole--and actors in the public policy ecosystem in particular--often fail to understand the systemic causes of complex social problems. The typical response tends to produce superficial and inefficacious policies, which frequently exacerbate the root causes of the problems (Sterman, 2000).

As the list of problems faced by modern societies and organizations keeps growing, there is a clear need for better policies. In this paper, using a conceptual repertoire drawn from the literature on system dynamics, complexity science and legitimacy, we present a framework with the goal of addressing that gap. Thus, the research question springs from the perceived need to cope with the systemic complexity in the policy space faced by organizations and governments. Specifically, we use the case of the Brazilian Worker Food Program (hereafter referred to as WFP) to illustrate the framework's contribution to the analysis of public policies. In other words, we aim to offer a systemic framework, using a real and relevant case, which can be used to analyze or produce better policies (Stroh, 2015), especially in complex systems. We believe this framework is sufficiently flexible to allow for the pursuit of different strategies of inquiry.

The paper is organized as follows. We begin by presenting the thresholds framework and outlining the methodology used in the study. Next, we apply the framework to the WFP, while presenting the context, characteristics and history of similar programs throughout the world. We conclude by discussing the results under the light of the proposed framework, acknowledging limitations and making suggestions for future studies.

Thresholds Framework

All public policies have a life cycle. The thresholds framework considers the relevance of thresholds in determining transitions of phase (i.e., growing, maturing and declining) during the life cycle of a public policy. Those thresholds involve both the physical aspects of a policy, such as the number of people reached in the different stages of implementation, and the symbolic aspects, such as legitimacy support for the policy. While in this paper we rely on system dynamics as the quantitative grammar of the framework, the elements drawn from complexity science are treated as conceptual lenses, in a qualitative fashion, with the goal of increasing the discernment of a policy's effects. To enhance the comprehensiveness of the framework, we incorporate elements usually associated with complexity science, namely power laws, networks and evolving ecosystems. Finally, we draw on legitimacy literature to understand how public policies make headway and on what basis challenges to their existence may arise over time. Therefore, as the thresholds are the backbone of our proposed framework (Figure 1), we follow with the three theoretical bodies: system dynamics, complexity science, and legitimacy.

Elements from system dynamics

System dynamics is a simulation method pioneered by Forrester (1961) and further described in works by Sterman (2000) and Ford (2010). It focuses on understanding the structures in a system that dynamically produce certain behavioral patterns. The method is especially suited for modeling complex social systems, where several actors interact under the influence of factors playing out over a long time horizon.

System dynamics propitiates a 10,000 meters view of the system, portraying its structures and policies in an aggregate manner. A staple tool in the repertoire of the field is the use of causal loop diagrams (CLDs), which illustrate the causal interrelationships among the different variables producing the phenomenon under analysis. This simulation method is essential to understanding dynamic complexity as commented by Ford (2010):

Climate change, pandemics, and boom and bust in real estate are complex dynamics that challenge our understanding. We are unable to anticipate the dynamic consequences of policies adopted today, especially when there are long delays between our actions and the system's reactions. Our understanding is also limited by the complexity of the feedback processes that control system behavior. Our actions may be partially erased by the system's internal responses, and the system's apparent resistance to our interventions is confusing. Sorting out the effects of delays and multiple feedbacks is beyond our cognitive abilities, so we look to the past for lessons. But how are we to interpret past patterns in climate change, pandemics and boom-and-bust cycles? Our understanding of the dynamics of historical patterns is limited by the same complexities that make it difficult to think about the future. There are many interpretations of past behavior, and we are left with limited understanding of both past trends and current problems (p. xi). From system dynamics literature, the major characteristics of social phenomena associated with public policies are endogeneity, delays, nonlinearities, and path dependence. Considering that many policies are ineffective or sub-optimal, we draw on the system archetypes stream from the same literature.

Endogeneity

The hallmark of system dynamics is its endogeneity viewpoint (Richardson, 1999). In this view, there is no simple, unidirectional relationship between variables, but usually a set of feedback loops linking all the variables together. Hence, the exogeneity that is part of common sense analysis is misleading. All variables are meshed in dynamical, reciprocal causal interrelationships. In this view, the dynamics of all systems arise from their internal structure. Self-reinforcing and balancing feedback loops define how the system behaves over time. Random perturbations, in turn, can be amplified by the system's feedback structure, creating different patterns in space and time. As Richardson (1999) puts it,

The concepts of feedback and circular causality are essential to reliable policy analysis. Experience with dynamic, nonlinear models of feedback systems repeatedly shows that failure to take account of existing feedback effects in the analysis of a policy initiative can cause exactly the wrong conclusions to be reached. More subtly, information feedback can be used to explain the observed tendency of social systems to be 'policy resistant', to react more weakly and more perversely to policy shifts than some experts predict (p. 4). Policy resistance is indeed a major characteristic of complex social systems, which is often ignored in the analysis of public policies. Thus, while accounting for first-order effects, there is a need to ponder the n-order effects that play out over a longer time span, as the agents in the system adapt to and respond to the interventions.

Delays

Not only do many problems take a long time horizon to manifest, but also the effects of public policies often are different as time unfolds. As an example of the former case, at the time of writing, Brazil has been facing a scorpion infestation that took two decades to unfold (Carvalho, 2019). The typical example of counterintuitive long-term effects of policies is road building to alleviate traffic congestion, in particular beltways that circle big cities, which produce improvements in the short term, only to lead to more vehicles on the roads over time (Morecroft, 2015; Sterman, 2000).

Thus, delays are a major hallmark of interventions in complex systems. The root causes of a complex problem, Stroh (2015) asserts, can be found in the many interdependent and often delayed relationships among the system's parts. Indeed, as hinted above, delays are a major reason behind the failure of usual policies. First, because all interventions produce consequences beyond the first-order effects their sponsors expect, as social agents try to adapt and resist the policies (Arthur, 2014; Sterman, 2000). Second, because the n-order effects usually take time to play out and are unanticipated. Thus, policies often produce misleading positive results in the short term (Sterman, 2000). Moreover, even when they go in the right direction, policies usually require a long-time horizon to manifest their results as material (e.g., financial resources, people) and immaterial stocks (e.g., reputation, brand image) slowly accumulate.

As a rule, the more units of time are accounted for in the analysis of a policy, the better.

Nonlinearities

Effects of policies are rarely proportional to cause (Sterman, 2012). In social systems, the interactions weaving together the different variables are typically nonlinear. For instance, the effect of the additional numbers of circulating cars on traffic ranges from mild to extreme, which means that, at some critical tipping point or threshold, adding a few cars to the streets is the recipe for monumental gridlocks. Nonlinearities thus define...

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