Public Policy Modeling and ApplicationsView this Special Issue
Review Article | Open Access
Policy Modeling and Applications: State-of-the-Art and Perspectives
The range of application of methodologies of complexity science, interdisciplinary by nature, has spread even more broadly across disciplines after the dawn of this century. Specifically, applications to public policy and corporate strategies have proliferated in tandem. This paper reviews the most used complex systems methodologies with an emphasis on public policy. We briefly present examples, pros, and cons of agent-based modeling, network models, dynamical systems, data mining, and evolutionary game theory. Further, we illustrate some specific experiences of large applied projects in macroeconomics, urban systems, and infrastructure planning. We argue that agent-based modeling has established itself as a strong tool within scientific realm. However, adoption by policy-makers is still scarce. Considering the huge amount of exemplary, successful applications of complexity science across the most varied disciplines, we believe policy is ready to become an actual field of detailed and useful applications.
Generally speaking, complex systems are those in which the sum of the parts is insufficient to describe the macroscopic properties of systems’ behavior and evolution [1–3]. Interactions among parts of the system, at different scales, in a nonhierarchical , nonlinear, and self-organizing manner  lead to emerging properties [6, 7] that fail to have a single, certain unfolding in the future.
Social actions, carried out by millions of individuals interacting in a multitude of way and through traditional or digital means, and economic processes, where highly heterogeneous economic actors are interconnected by transactions, ownership relations, competition, and mutualism, are two paradigmatic kinds of complex systems. Policies, as a set of actions to enhance social life and economic processes, are an archetypical example of controlling them. Policies are the product of the interaction of agents and institutions in time and space in which knowledge of current state provides only incomplete views of future states of the system. Policy modeling, as an attempt to design the operation of such interactions, presupposes some level of comprehension of the mechanisms, processes, and likely trajectories while maintaining a strict knowledge of the inherent incompleteness of modeling complex systems [8–12].
This view that policies are complex enables the application of complex systems’ methodologies onto the analysis of public (and private) policy-making. Such application feeds on early contributions and takes many forms that vary from simple construction of indicators and measures of complexity à la Shannon [13–15], to cellular automata and artificial intelligence , to agent-based modeling  and network science .
This review provides an overview of contemporary applications of policy modeling that follows the traditional complex systems’ methodologies portfolio. Mainly, we focus on agent-based modeling, network science, and data mining. First, we discuss the methodologies themselves and then we examine three cases in detail. A large consortium for infrastructure analysis applied to the case of Britain, one of the most consolidated families of macroeconomics agent-based models and its applications on fiscal and monetary policies and a land-use model in use by metropolitan governance entities across the USA and abroad. Implications for policy modeling close the paper.
2. Policy Modeling Methodologies
Fuentes  describes a landscape of eleven distinct methodologies coming from complex systems’ sciences (the complex science referred by Fuentes [19, pp. 55–56] include “nonlinear science, bifurcation theory, pattern formation, network theory, game theory, information theory, super statistics, measures of complexity, cellular automata, agent-based modeling, and data mining”). While that approach is more exhaustive, here, we emphasize applications to public policies, thus focusing more extensively on the discussion of agent-based modeling and cellular automata, data mining, network analysis, and game theory. Other relevant methodologies are considered together in a specific subsection.
2.1. Agent-Based Modeling and Cellular Automata
Agent-based modeling (ABM) is a computational or algorithmic, artificial implementation of agents who interact among themselves and with the environment following a set of rules. As a result of the interaction, the variables that describe the state of the agents may be modified [20–23]. ABM is useful when analytical solutions are too complex or impossible to be calculated. Results are sensitive to initial conditions, although, in many cases, still deterministic, and thus useful interpretation relies on distributions of stochastically repeated simulations of the model and reasonable validation.
The uniqueness of ABM methodology led Epstein [24, 25], probably inspired by Ostrom , to suggest a third way of doing science. Verbal or argumentative would be the first one, mathematically quantification a second, and algorithmic simulation the third . Such proposal is in line with the views of philosopher Nicolescu  in which social sciences (and therefore, policy-making) have a contained maneuvering space when experimenting with populations and individuals. ABM provides just that liberty of experimenting in silico with additional degrees of freedom. Hence, ABM may enhance the capabilities of social sciences to bridge science and policy.
In fact, agent-based modeling as a methodology has a number of attributes that probably make it the method of complex systems most attached to policy-making. It is flexible, adaptable to empirical analysis, cost-effective, and adequate to ever-changing analysis scenarios . ABM is also applied to a number of different disciplines in the realm of policy studies from demography , to anthropology , and it also gains recognition and scope in economics [27, 32] and international politics . Finally, there is also a profusion of available tools specifically designed for modeling .
In economics, Dawid and Delli Gatti  categorize seven distinct families of macroeconomic models [36–44]. Dosi, Fagiolo, Roventini, and coauthors [41, 45–47] probably lead the most prolific evolutionary branch of economic modeling whereas Lengnick is a single standalone model proposal which does not include a credit market . Despite these large macroeconomic models, economic studies also emphasize market-specific models: in electricity [48, 49], labor market [47, 50], economic behavior [51, 52] and the problem of commons . Further in economics, ABM is used to criticize current, quantitative, yet perfect (without endogenous crises) economic models [54–56]. Finally, it is worth mentioning the use of ABM to test policies applied to the financial markets, from the early contributions of LeBaron [57, 58] and Westerhoof  to the detailing of the effects of transaction taxes and trading halts on assets volatility .
Despite all these contributions, institutions and governments are still slow in adopting recommendations. Bank of England , OECD , the European Union  and some academic institutions (such as MITRE, DARPA, and NECSI) have helped fuel the debate. Although Page  reminds us with the essence of understanding the mechanism underpinning economic phenomena, policy-makers do not seem ready to accept general results and explanations [65–67], rather sticking to precise (yet probably wrong ) numbers.
Cellular automata (CA) also goes back to the infancy of complex studies [3, 6, 68, 69]. Its fundamental design is the related analysis of diffusion by contact processes in a deterministic way when the state of the agents can be described by a finite set of states. Despite a stream of literature by itself, contemporary conceptualization of CA may consider it as a special case of agent-based modeling in which agents are fixed and not mobile and their relationships follow a matrix of adjacency [70, 71]. Even then, CA is much used for spatial analysis, having once been called “space theory based models” .
In fact, among geographers and spatial analysts “Land-Use and Transportation” models (LUTs) have yielded results and recommendations for the past two decades. Early models [73, 74] focused on urban development, but those were readily followed by more general land-cover models [75, 76]. Transportation and activities models also intensified their uses in the 2000s [77, 78] and became paradigmatic for actual use in urban planning and transportation [79–81], especially by metropolitan bodies.
Advances in the area have been so intertwining that new models have started to feed from different trends of spatial-modeling literature bridging transportation and land-use models to macroeconomics  and actual life-cycle of individuals in order to generate individual demand models . The bridge has also been generous when crossing automated computing techniques and traditional models  or when aiding its validation .
2.2. Network Science
Network science studies the structure of a given system in general by recourse of tools originated in graph theory. Here, nodes describe the system constituents and edges conform the interactions between them. One may consider again that networks are generalizations of agent-based models in which not only agents have attributes, but also edges, with varying attributes and lengths. These connections are abstractions, or dimensional relationships of the typical fixed neighborhood found in CA models. These arguments are purely conceptual and only aid the highlighting of the complementarity of complex systems methodologies . In fact, two similar economic models may use either networks  or spatial distance  as rules towards consumer decision-making.
Despite this inherently attached connection with both ABM and CA, network science has come a long way after its relatively recent birth as an independent yet gregarious discipline, marked by the seminal papers by Watts and Strogatz  and Barabási and Albert . In fact, it has developed a large field of literature that has evolved from recent work on network statistics  to studies that describe dynamic changes of the network itself .
In Finance, applied network analysis has helped illuminate likely policy effects of systemic risk within interbank trading. An early work by Battiston et al. [91, p. 2082] showed that local interactions travelling within networks may function as “an alternative mechanism for the propagation of failures”. Subsequent work was able to measure network systemic relevance more precisely  and thus apply policy testing, including transparency advocacy  and leverage regulation . Together, these analyses have demonstrated with considerable easiness the possibilities of simulating alternative policy scenarios.
A current challenge in network science is “understanding the relationship between structure and function” [95, p. 9]. Scientists are trying to comprehend how the topological structure of a given network—how their nodes are connected–influences their systemic functions and what they do. An example would be to clarify how the connection of proteins determines a resulting phenotype. Conversely, others  are trying to find the function or purpose of the network given observed data, having applied examples on migration, congress voting, and the human brain.
2.3. Data Mining
Data mining, or more generally data science, has benefited from continuously decreasing hardware prices, larger software communities, and an abundance of data following generalization of desktops first and mobile devices more recently, which subsumed giving rise to the ongoing digitalization of society. One could date this quantification and empirical emphasis back to around 2001’s book by Hastie, Tibshirani, and Friedman , deepening the effort in 2009 . Quickly, deep learning  and neural networks  became standard, maybe due to available software (and accelerators), such as TensorFlow .
There is no doubt of the beneficial effects of data science on social life enhancement in fields such as pinpointing fraudulent actions [102, 103] helping in medicine diagnostics , training of professionals via simulation , or aiding mobility through autonomous systems . However, some concerns are also present . Specifically, there are mentions of results without theory, like the infamous Garbage In, Garbage Out (GIGO) .
Such lack of theory is of minor concern for some machine learning scientists who want solely to achieve the best possible prediction, no matter the processes or prejudices. Conversely, there is the argument that complex “description” , hitherto unavailable, may provide new theories by induction, which previously seemed as a lackluster source of scientific reasoning.
2.4. Game Theory
Game theory focuses on how a group of agents or elements (which may describe individuals, organizations, economic actors, etc.) interact using strategic decision-making. The two branches that one can visualize in this field are cooperative or noncooperative. A good reference for a discussion on this topic can be seen in . It is usually understood that cooperative game theory is applicable when agreements are enforceable, while noncooperative game theory is applicable otherwise. McCain argue that noncooperative game theory is an effective tool for problem-finding (or diagnostic method). These observations make game theory a useful methodology to be applied in societies that faces continuous decision-making processes. Moreover, recent studies suggest that a combination of game theory with psychology and neuroscience has great potential to understand mechanism involved in social decision-making . It is worth mentioning that the connection between game theory and complexity can be achieved, or it is clearer, when an important number of agents are connected in a network interacting under a game theory dynamics, as in the case of evolutionary game theory .
2.5. Other Policy Methodologies
Dynamical systems (DS) is a modeling approach in which there are timed flows among stocks and control of probabilistic input of variables in order to conform a systemic analysis with feedback . On such setting, each stock entity is an abstract construction that allows mathematical simulation of future states of the system. There is not, however, heterogeneity within each entity as in ABM, nor spatial representation, as Batty reminds us . DS was introduced in the 1970s  and has accumulated many applications and supporting technologies since then . Even though DS and ABM share some characteristics, they also have important, fundamental, differences . Some of those are the applications on different levels of analyses using ABM on complex networks . In those types of systems, the emergence of new characteristics at higher levels is difficult to analyze using first principles, something that is one of the main characteristics and properties of classical DS .
Moreover, numerical simulation, microsimulation, or yet mathematical simulation is also a methodology that solves otherwise intractable analytical equations numerically . It is the simple application of known rules, usually probabilistic ones, to known states so that the researcher can observe the trajectory and results into the future. Numerical simulation is useful, for example, to understand the effects of a given tax change on specific sectors or taxpayers. Crooks and Heppenstall , however, highlight that microsimulation accounts only for direct effects, the effect of taxes on the market, but not the counterreaction, i.e., the indirect effect of having a market that is different from the original one. Numerical simulation has been used in various fields ranging from regional economics , to fluids, to pollution.
Before concluding, we highlight that this review is definitely not exhaustive, but covers the methodologies most attached to policy applications. We describe some applications in the following section.
3. Policy Modeling Applications
There has been a wide and spread range of research and output for policy using complex systems’ various methodologies. This special issue is, as far as we know, a first effort to put together the main strands of literature specifically on the area of policy. On the same vein, the previous section lists a sample of the most referenced publications that discusses policy modeling and applications in order to help crisscross leading researchers over different fields. In this section, we dwell a bit longer on three cases of larger impact and reverberation, in our opinion.
Specifically towards policy and management, there is significant difference between planning for a known, well-designed trajectory development and planning within a complex system environment. “Complexity theory demonstrates that there are fundamental conceptual difficulties in the concepts of “planning” in any open system which contain a significant level of decentralization of decision-making”  [122, p. 320]. In other words, referring to fishery governance, systems are neither predictable nor controllable , thus, the need to consider ever-changing environments when doing policy-making.
3.1. System of Systems
A consortium of seven leading universities and other partners in the United Kingdom has formed the Infrastructure Transitions Research Consortium (ITRC). ITRC has put together a National Infrastructure Model (NISMOD) that in turn has evolved into a current program named Multiscale Infrastructure Systems Analytics (MISTRAL). All those acronyms depict a large institutional effort aimed at applying “complexity-based methods” to public policy based on a criticism of reductionist science, systems theory, and mainstream neoclassical economics.
ITRC proposed focusing on four main themes (according to the Report “Final Results from the ITRC” (2015), available at https://www.itrc.org.uk/wp-content/PDFs/ITRC-booklet-final.pdf. For a longer discussion, see ):(a)Develop a capacity to compare quantitative metrics of infrastructure capacity and demand given a varied number of alternative scenarios, which ITRC call national strategies, while accounting for interdependent effects among infrastructure sectors(b)Develop a specific model of vulnerabilities and cascading cumulative failures (and resilience) in connected infrastructure systems(c)Develop an understanding of the dynamics of infrastructure when coupled with evolving socioeconomic (heterogeneous), spatially specific social groups(d)Develop sound long-term planning of infrastructure systems
MISTRAL proposes going further with four encompassing challenges (see Report ): (a) downscale, detail, and emphasize local complexity of infrastructure, (b) focus on its interdependencies and connections, (c) take the experience abroad and change infrastructure decision-making internationally, and (d) maintain its focus on quantifying the relationships between infrastructure and economic growth.
All in all, the best output to follow the production of ITRC’s proposal is the book by leading researchers Jim W. Hall, Martino Tran, Adrian J. Hickford, and Robert J. Nicholls . The authors introduce the book motivating the relevance of infrastructure systems, discussing the challenges of handling infrastructure within a contemporary, advanced-economies, interdependent environment, and outlining their “system of systems approach”.
Such an approach, the authors claim, would equalize both assumptions and metrics across different infrastructure sectors and make them robust against future uncertainties, all in accordance with strategies developed and scenarios that are outside the control of policy-makers. Further, the system of systems would be able to capture possible risks and vulnerabilities, thus making the infrastructure system more resilient. Hence, better, long-term planning would ensue.
According to their proposed framework, the first methodological step is scenario generation. A scenario builds upon a range of possible futures unfolding from demographic, economic, climate change, and environmental alternatives. As a result, “a complete set (times series) of external parameters defining the boundary conditions” [126, p. 15] is produced. The total number of possible scenarios considering all alternatives amounts to 2,112 combinations. However, given that difference, close scenarios provide very similar results and have different probabilities; in practice a set of three most likely scenarios with some variants is actually employed in the analysis.
Next, strategies, defined as the possible ways to tackle infrastructure provision in terms of planning, investment, and projects, are developed. The proposals need to be based on national policy directives and detailed enough so that they can be simulated. At the same time, three approaches per sector are observed: (a) demand management, such as regulation of a given sector, which may affect demand; (b) system efficiency, including possible gains that come from technology adoption for instance, and (c) capacity expansion changes, which actually involves physically altering infrastructure assets.
The third methodological step implies the use of detailed models that are specific for each sector, but that are intertwined with one model’s input coming from another model’s output. The best description of the system of systems method is that of a “family of models” in which communication and special links are consistent so that policy trade-offs across the full infrastructure system are properly evaluated.
Qualitatively, there are four ways to incorporate changes within the model. When policy-makers propose a change of policy, such a proposal enters the model as a strategy-change, which will end up as a measured change in demand. When incorporating an innovative process, efficiency parameters change. When physical infrastructure changes, the capacity (supply) of the system changes. Finally, when exogenous change happens, scenarios also change. When all the above steps have been implemented, the system is ready to provide evaluation and prognostics. As the authors claim, a “web-based data-viewer (…) combines and compares performance across sectors, across time, across regions, and across future conditions” [126, p. 24].
The project has certainly spanned a wide range of results and publications (a full reference guide, divided by nine themes (complex adaptive systems, databases, demographics, digital communications, economic impacts, governance, infrastructure system network risk analysis, and solid waste) can be found at https://www.itrc.org.uk/outputs/research-outputs/). Among the ones with larger reach is a methodological proposal (with 25 authors)  and a study on the link between energy and water .
3.2. Macroeconomics Agent-Based Model
One of the seven families of macroeconomics models described by Dawid and Delli Gatti  and also featured in Dosi and Roventini  is coined “Keynes meets Schumpeter” (KS) and was led by Dosi, Fagiolo, Roventini, among other coauthors (we have chosen this family to detail, as it seems to have the larger number of stylized facts reproduced and the more proficuous production). The model baseline was described in  and has a recent consolidation in . A new model validation proposal uses KS as case-study  and policy applications have been done on climate change , labor market [46, 132], and monetary policy [133, 134]. A review on macroeconomics agent-based models policy application is available in .
The great contribution of KS is the ability to endogenously reproduce long-term economic cycles , while also maintaining short-term results, thus, breaking with the economic paradigm of equilibrium in which crisis is only deviations from a supposedly correct natural path . Crisis in fact may have long-lasting effects on the economy. Further, the KS model seems especially relevant given that it has been shown to be validated . As such, KS refutes the main valid criticism of agent-based modeling which is the lack of validation. Further, it provides actual policy case studies that are concrete and foundational enough to be applied to policy-making.
KS models the attempt of translating conceptual innovation theory into measured, validated output growth, contextually dependent on macroeconomic conjuncture. Such an attempt covers both the short-term (and indicators such as unemployment) and the long run (such as GDP). KS approach is novel, when compared to traditional macroeconomic Dynamic Stochastic General Equilibrium (DSGE) models as traditional ones do not treat technology as an endogenous factor for model explanation .
The model is composed of firms from two sectors (capital and consumption-goods), banks, Central Bank, labor force, and government. Capital firms drive innovation when investing in R&D and output more efficient, cheaper machines. Government defines taxes values and unemployment subsidy levels and the Central Bank decides on interest rate levels .
The authors list 17 stylized facts, in both macro- and microeconomics, that help ensure the model validity for policy analysis. Those stylized facts go from replicating endogenous economic fluctuations, to recession durations to cross-correlation of macro- and credit-related variables. Further, in microeconomics, the model mimics firms size distribution, firms’ productivity heterogeneity, bankruptcies, among others . Despite these resemblances, as previously mentioned, KS model was used as a case-study by [85, p. 138, our emphasis] in which they compared the structures of vector autoregressive models in a five-step process to show that KS “resemble between 65% and 80% of the causal relation” in observed macroeconomic time-series.
A bundle of eleven policy analyses derived from KS is available [46, 133, 134]. They include firms’ innovation search capabilities, technological opportunities, patents, new firms’ productivity, market selection, antitrust, among others. Specifically about income inequality,  reports that markup setting influences both the dependence of financial support and the share between profits and wages. Such mechanism affects macroeconomics’ stability and growth. Together, the authors agree with inputs by Stiglitz and Piketty that claim that there is a downward trend feedback loop in economics that can be tied to higher levels of inequality. In such a scenario fiscal austerity has more negative effects when coupled with higher markup levels.
Furthermore, recent applications of the KS model on the labor market [47, 136, 137] have helped show that more rigid markets with higher levels of protection and less flexible wage may in fact keep output at increased levels, while maintaining lower inequality. Dosi et al.  suggest that coordination failures bring wages down and thus significantly impacting aggregate demand. The authors also suggest  that the core reasons of rising unemployment are lower innovation rates, workers’ skills deterioration, and reduced firms entry dynamics. This is also relevant because it goes against typical policy recommendations based on DSGE applications hitherto supported by international institutions such as OECD and the IMF, but which is under discussion .
In sum, KS model suggests that (a) technological changes and market open for new firms lead to strong positive growth; (b) patent enforcement, however, reduces growth dynamism; and (c) competition is relevant, but producing weaker effects. KS further recommends countercyclical fiscal policy (opposed to fiscal austerity) as a means to convey (a) unemployment and output stability; (b) “higher growth paths”, whereas fiscal austerity would be detrimental to the economy and government debt in both the short and long run.
KS is thus a model that clearly exemplifies a new tradition for a given segment of macroeconomics . It seems to have been able to capture most of the variations of a complex system, the economy, allowing for policy analysis in a validated, fact replicating reasonable model.
UrbanSim was first proposed in the mid-1990s [77, 78, 139, 140] as a model that focused on feedback effects from land use into transport and back. Their modeling process include a family of submodels that run independently, feeding on empirical data, while exchanging inputs and outputs among themselves. In practice, a GIS interface with a typical 150-meter grid works as a single unit summing up households and firms. The land price model follows typical neoclassical urban economics  and hedonic price regression modeling  which is update at each year-step of the model.
Since then, UrbanSim has grown and changed into a 3D platform in 2012 and became proprietary software (urban canvas) in 2014. They have now expanded the simulation to include not only land use and transportation but also the economy and the environment. They claim to account for unfolding effects of infrastructure onto transport, housing affordability, and the environment.
The agents of the model include households, individual citizens, and firms, including land developers, government, and their political constraints . Facing a given environment, agents make choices, such as (a) whether to find a job (and which one) or stay at home, (b) where to locate your own family or your business, and (c) whether a family or business relocation is a sensible move.
The market in the model is asynchronous. Households search for new dwellings facing the short-term needs for a given year. Land developers observe house demand, but augment house supply within a wider, cumulative number of years . Although prices are modeled (calculated) endogenously, the model does not impose equilibrium; i.e., there is no need for markets to “clear” .
A first application of the model is available for Eugene-Springfield, Oregon . The interested policy stakeholder, Oregon Department of Transportation, actually financed the project. The model uses data starting in 1994 and runs for 15 years with a resolution grid of 150 square meters. A previous time-frame from 1980 to 1994 was used to validate the model capabilities. A good accuracy (of less than 50 households between simulated and observed results) was achieved for 57% of the sample. A larger error of up to 200 households included 89% of the sample for number of households and 76% for the number of jobs. Although reasonable, the results did not predict isolation and pinpoint change occurrences and have also overpredicted and underpredicted for small areas.
The takeaway from the first UrbanSim implementation is that effective integration of at least land use, transportation, and environmental issues’ mechanisms is a must . Such an integration should also be accompanied by considering that, in practice, those areas usually involve different, distinct institutions (responsible for each issue), with conflicting values, epistemologies, and pragmatic policies .
The belief for integrated planning shown in the 2011s  paper is still present in UrbanSim’s most recent output . Once more financed by an interested policy stakeholder, US Department of Energy, the authors propose an integrated “pipeline” among UrbanSim, called a microsimulation platform, along with ActivitySim, an agent-based model platform responsible for the generating traffic demand based on citizens choice of activities and a traffic assignment model (a routing mechanism). The motivation behind the attempt is clear: how to effectively quantify both intended and unintended consequences on urban complex environments, given a specific change in infrastructure or policy. Further, as the authors put it, urban systems are those in which “transportation network, the housing market, the labor market ([via] commuting), and other real estate markets are closely interconnected...” [79, p. 2].
In this review, we show some of the impact of the complexity science methodologies has had in public policy. We take special attention to agent-based modeling, network science, data mining, and game theory. We believe that these methodologies are important not only being used extensively nowadays, but also being the ones that are creating the bridge between science (its quantitative and qualitative methods, ways of thinking, etc.) and policy decision-making. We also have presented real cases where the use of such methodologies has been used.
The case analysis suggests that larger efforts are being developed in policy applications of different realms, from macroeconomics (fiscal and monetary policy), to urban planning (mobility and air pollution), to infrastructure (energy, water, and waste). We have selected these examples as they are paradigmatic, while we acknowledge that they are only some of the high-magnitude works currently being developed. The list of references amassed account for other smaller, scattered applications that seem to be wide and spread across disciplines.
Nevertheless, such a growing body of literature does not show that these methodologies have been understood, nor accepted without caveats in academia (in general) and policy-makers. Namely, most macroeconomics policy follows DSGE methods although they have been also heavily criticized . Most infrastructure projects are planned in an isolated manner, following sector guidelines with little or no interface with other sectors. Further, most urban planning carried out observes all of the challenges issues listed by  conflicting institutions, values, epistemologies, and policies, but also the inherent communication issues across heterogeneous fields.
All in all, results of the three cases presented in detail reinforce the belief that integrated modeling performed with input originated across disciplines, sectors, and institutions within a complex systems framework deliveries with the added bonus of unveiling large scale effects of policies in an adaptive, evolutionary, nonhierarchical manner.
This work is part of a special issue on Public Policy Modeling and Applications.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Bernardo A. Furtado would like to acknowledge Grant from the National Council of Research (CNPq). Claudio J. Tessone acknowledges financial support of the University of Zurich through the University Research Priority Program on Social Networks.
- P. W. Andersen, “More is different,” Science, vol. 177, no. 4047, pp. 393–396, 1972.
- C. G. Langton, “Studying artificial life with cellular automata,” Physica D: Nonlinear Phenomena, vol. 22, no. 1–3, pp. 120–149, 1986.
- J. V. Neumann, “The role of high and of extremely high complication,” in Theory of self-reproducing automata, pp. 64–87, University of Illinois Press, Urbana, 1966.
- H. A. Simon, “The Organisation of Complex Systems,” in Hierarchy Theory - the challenge of complex systems, H. H. Pattee, Ed., pp. 1–27, New York, NY, USA, 1973.
- S. Wolfram, “Universality and complexity in cellular automata,” Physica D: Nonlinear Phenomena, vol. 10, no. 1-2, pp. 1–35, 1984.
- A. M. Turing, “The chemical basis of morphogenesis,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 237, no. 641, pp. 37–72, 1952.
- J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 79, no. 8, pp. 2554–2558, 1982.
- R. Geyer and P. Cairney, Handbook on Complexity and Public Policy, Edward Elgar Publishing, 2015.
- B. Edmonds, “The room around the elephant: tackling context-dependency in the social sciences,” in Non-equilibrium social science and policy, J. Johnson, P. Ormerod, Y.-C. Zhang, and A. Nowak, Eds., pp. 195–208, Switzerland, 2017.
- D. Helbing, Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior, Springer, New York, NY, USA, 2012.
- J. Johnson, P. Ormerod, B. Rosewell, A. Nowak, and Y.-C. Zhang, Non-Equilibrium Social Science and Policy, Springer, 2017.
- B. Mueller, “Complex systems modelling in Brazilian public policies,” in Modeling complex systems for public policies, pp. 261–278, IPEA, Brasília, 2015.
- C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol. 27, no. 4, pp. 623–656, 1948.
- A. N. Kolmogorov, “Three approaches to the quantitative definition of information,” Problems of information transmission, vol. 1, no. 1, pp. 3–11, 1965.
- M. Gell-Mann and S. Lloyd, “Effective Complexity,” in Nonextensive Entropy, pp. 387–398, Murray Gell-Mann and Constantino Tsallis, 2004.
- M. Minsky, “Steps Toward Artificial Intelligence,” Proceedings of the IRE, vol. 49, no. 1, pp. 1–65, 1961.
- T. C. Schelling, “A process of residential segregation: neighborhood tipping,” in Racial discrimination in economic life, vol. 157, p. 174, 1972.
- M. E. J. Newman, “The structure and function of complex networks,” SIAM Review, vol. 45, no. 2, pp. 167–256, 2003.
- M. Fuentes, “Methods and methodologies of complex systems,” in Modeling complex systems for public policies, pp. 55–72, IPEA, Brasília, DF, 2015.
- J. M. Epstein and R. L. Axtell, Growing artificial societies: social science from the bottom up, Brookings/MIT Press, Cambridge, MA, 1996.
- M. Batty, “A generic framework for computational spatial modelling,” in Agent-Based Models of Geographical Systems, pp. 19–50, Springer, 2012.
- L. Tesfatsion, “Agent-Based Computational Economics: A Constructive Approach to Economic Theory,” in Handbook of Computational Economics, vol. 2, pp. 831–880, Elsevier, 2006.
- U. Bilge, “Agent based modelling and the global trade network,” in Handbook on complexity and public policy, Robert Geyer and Paul Cairney, pp. 414–431, 2015.
- J. M. Epstein, “Agent-based computational models and generative social science,” Complexity, vol. 4, no. 5, pp. 41–60, 1999.
- J. M. Epstein, “Remarks on the Foundations of Agent-Based Generative Social Science,” in Handbook of Computational Economics, vol. 2, pp. 1585–1604, Elsevier, 2006.
- T. M. Ostrom, “Computer simulation: The third symbol system,” Journal of Experimental Social Psychology, vol. 24, no. 5, pp. 381–392, 1988.
- P. Terna, “From complexity to agents and their models,” in Agent-based models fo the economy: from theories to applications, B. Riccardo, M. Matteo, S. Michele, and T. Pietro, Eds., pp. 10–30, 2015.
- B. Nicolescu, Manifesto of transdisciplinarity, Suny Press, 2002.
- B. A. Furtado, P. A. M. Sakowski, and M. H. Tóvolli, Modeling complex systems for public policies, Brasíli, IPEA, 2015.
- F. C. Billari, F. Ongaro, and A. Prskawetz, “Introduction: Agent-based computational demography,” in Agent-Based Computational Demography, pp. 1–17, Springer, 2003.
- J. S. Dean, G. J. Gumerman, J. M. Epstein et al., “Understanding Anasazi culture change through agent-based modeling,” Dynamics in human and primate societies: Agent-based modeling of social and spatial processes, pp. 179–205, 2000.
- L. Hamill and N. Gilbert, Agent-based modelling in economics, John Wiley & Sons, UK, 2016.
- R. Geyer and S. Rihani, Complexity and public policy: a new approach to 21st century politics, policy and society, Routledge, London, UK, 2010.
- S. Abar, G. K. Theodoropoulos, P. Lemarinier, and G. M. P. O'Hare, “Agent Based Modelling and Simulation tools: A review of the state-of-art software,” Computer Science Review, vol. 24, pp. 13–33, 2017.
- H. Dawid and D. D. Gatti, “Agent-Based Macroeconomics,” in Handbook on Computational Economics, vol. 4, Elsevier, 2018.
- D. D. Gatti, C. Di Guilmi, E. Gaffeo, G. Giulioni, M. Gallegati, and A. Palestrini, “A new approach to business fluctuations: Heterogeneous interacting agents, scaling laws and financial fragility,” Journal of Economic Behavior & Organization, vol. 56, no. 4, pp. 489–512, 2005.
- Q. Ashraf, B. Gershman, and P. Howitt, “Banks, market organization, and macroeconomic performance: An agent-based computational analysis,” Journal of Economic Behavior & Organization, vol. 135, pp. 143–180, 2017.
- H. Dawid, S. Gemkow, P. Harting, S. van der Hoog, and M. Neugart, “An Agent-Based Macroeconomic Model for Economic Policy Analysis: the Eurace@ Unibi model,” Bielefeld Working Papers in Economics and Management, 2014.
- S. Cincotti, M. Raberto, and A. Teglio, “Macroprudential policies in an agent-based artificial economy,” Revue de l'OFCE, vol. 124, no. 5, pp. 205–234, 2012.
- P. Seppecher, “Flexibility of wages and macroeconomic instability in an agent-based computational model with endogenous money,” Macroeconomic Dynamics, vol. 16, no. 2, pp. 284–297, 2012.
- G. Dosi, G. Fagiolo, and A. Roventini, “The microfoundations of business cycles: an evolutionary, multi-agent model,” in Schumpeterian Perspectives on Innovation, Competition and Growth, U. Cantner, J.-L. Gaffard, and L. Nesta, Eds., pp. 161–180, Springer, Berlin, Heidelberg, 2009.
- M. Lengnick, “Agent-based macroeconomics: a baseline model,” Journal of Economic Behavior & Organization, vol. 86, pp. 102–120, 2013.
- A. Haas and C. Jaeger, “Agents, bayes, and climatic risks - A modular modelling approach,” Advances in Geosciences, vol. 4, pp. 3–7, 2005.
- Q. Ashraf, B. Gershman, and P. Howitt, “How inflation affects macroeconomic performance: An agent-based computational investigation,” Macroeconomic Dynamics, vol. 20, no. 2, pp. 558–581, 2014.
- G. Dosi, G. Fagiolo, and A. Roventini, “An evolutionary model of endogenous business cycles,” Computational Economics, vol. 27, no. 1, pp. 3–34, 2006.
- G. Dosi, G. Fagiolo, and A. Roventini, “Schumpeter meeting Keynes: a policy-friendly model of endogenous growth and business cycles,” Journal of Economic Dynamics & Control, vol. 34, no. 9, pp. 1748–1767, 2010.
- G. Dosi, M. C. Pereira, A. Roventini, and M. E. Virgillito, “The Effects of Labour Market Reforms upon Unemployment and Income Inequalities: An Agent Based Model,” Socio-Economic Review, 2016.
- W. Li and L. Tesfatsion, “Market provision of flexible energy/reserve contracts: Optimization formulation,” in Proceedings of the IEEE Power and Energy Society General Meeting (PESGM '16), pp. 1–5, Boston, MA, USA, July 2016.
- F. Kühnlenz, P. H. J. Nardelli, S. Karhinen, and R. Svento, “Implementing flexible demand: Real-time price vs. market integration,” Energy, vol. 149, pp. 550–565, 2018.
- M. Neugart and M. Richiardi, Agent-based models of the labor marke, vol. 125 of LABORatorio R. Revelli working papers series, 2012.
- W. B. Arthur, “Designing economic agents that act like human agents: A behavioral approach to bounded rationality,” The American Economic Review, pp. 353–359, 1991.
- P. Albin and D. K. Foley, “Decentralized, dispersed exchange without an auctioneer. A simulation study,” Journal of Economic Behavior & Organization, vol. 18, no. 1, pp. 27–51, 1992.
- E. Ostrom, “Beyond markets and states: Polycentric governance of complex economic systems,” American Economic Review, vol. 100, no. 3, pp. 641–672, 2010.
- G. Fagiolo and A. Roventini, “Macroeconomic policy in DSGE and agent-based models redux: New developments and challenges ahead,” Journal of Artificial Societies and Social Simulation, vol. 20, no. 1, 2017.
- P. Krugman, “The profession and the crisis,” Eastern Economic Journal, vol. 37, no. 3, pp. 307–312, 2011.
- J. E. Stiglitz, “Rethinking macroeconomics: What failed, and to how repair it,” Journal of the European Economic Association, vol. 9, no. 4, pp. 591–645, 2011.
- B. LeBaron, “Building the Santa Fe Artificial Stock Market,” Working Paper, Brandeis University, 2002.
- B. LeBaron, Agent-based computational finance, vol. 2, Elsevier, 2006.
- F. H. Westerhoff, “The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies,” Jahrbücher für Nationalökonomie und Statistik, vol. 228, no. 2-3, pp. 195–227, 2008.
- S. J. Leal and M. Napoletano, “Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading,” Journal of Economic Behavior & Organization, 2016.
- S. Burgess, E. Fernandez-Corugedo, C. Groth et al., “The Bank of Englands forecasting platform: COMPASS, MAPS, EASE and the suite of models,” Working Paper 471, 2013.
- OECD, Systems Approaches to Public Sector Challenges, Organisation for Economic Cooperation and Development, Paris, France, 2017.
- C. Deissenberg, S. van der Hoog, and H. Dawid, “EURACE: a massively parallel agent-based model of the European economy,” Applied Mathematics and Computation, vol. 204, no. 2, pp. 541–552, 2008.
- S. E. Page, Diversity and complexity, Princeton University Press, 2010.
- M. Mitchell, Complexity: A Guided Tour, Oxford University Press, New York, NY, USA, 2011.
- R. Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton University Press, 1997.
- R. Solé and B. Goodwin, How complexity pervades biology, Basic, New York, NY, USA, 2000.
- S. Wolfram, “Statistical mechanics of cellular automata,” Reviews of Modern Physics, vol. 55, no. 3, pp. 601–644, 1983.
- T. C. Schelling, “Models of segregation,” The American Economic Review, vol. 59, no. 2, pp. 488–493, 1969.
- U. Wilensky and W. Rand, An introduction to Agent-Based Modeling, The MIT Press, Cambridge, Massachusetts, 2015.
- M. Batty, Cities and complexity: understanding cities with cellular automata, agent-based models and fractals, The MIT Press, Cambridge, MASS, USA, 2005.
- L. An, “Modeling human decisions in coupled human and natural systems: review of agent-based models,” Ecological Modelling, vol. 229, pp. 25–36, 2012.
- R. White, G. Engelen, and I. Uljee, “The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics,” Environment and Planning B: Planning and Design, vol. 24, no. 3, pp. 323–343, 1997.
- R. White and G. Engelen, “Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns,” Environment and Planning A, vol. 25, no. 8, pp. 1175–1199, 1993.
- D. C. Parker, S. M. Manson, M. A. Janssen, M. J. Hoffmann, and P. Deadman, “Multi-agent systems for the simulation of land-use and land-cover change: a review,” Annals of the Association of American Geographers, vol. 93, no. 2, pp. 314–337, 2003.
- T. Filatova, D. Parker, and A. van der Veen, “Agent-based urban land markets: Agent's pricing behavior, land prices and urban land use change,” Journal of Artificial Societies & Social Simulation, vol. 12, no. 1, 2009.
- P. Waddell, “Urbansim: Modeling urban development for land use, transportation, and environmental planning,” Journal of the American Planning Association, vol. 68, no. 3, pp. 297–314, 2002.
- P. Waddell and G. F. Ulfarsson, “Dynamic Simulation of real estate development and land prices within an integrated land use and transportation model system,” in Proceedings of the Transportation Research Board 82nd Annual Meeting, p. 21, Washington, DC, USA, 2003.
- P. Waddell, G. Boeing, M. Gardner, and E. Porter, “An Integrated Pipeline Architecture for Modeling Urban Land Use, Travel Demand, and Traffic Assignment,” 2018, https://arxiv.org/abs/1802.09335.
- H. van Delden, R. Vanhout, M. Te Brommelstroet, and R. White, “Design and development of integrated spatial decision support systems: applying lessons learnt to support new town planning,” in Model Town: using urban simulation in new town planning, SUN, Amsterdam, Holanda, 2009.
- A. Horni, K. Nagel, and K. Axhausen, The Multi-Agent Transport Simulation MATSim, Ubiquity Press, London, UK, 2016.
- B. A. Furtado, PolicySpace: agent-based modeling, IPEA, Brasília, 2018.
- E. Galli, L. Cuéllar, S. Eidenbenz, M. Ewers, S. Mniszewski, and C. Teuscher, “ActivitySim: Large-scale agent-based activity generation for infrastructure simulation,” in Proceedings of the Spring Simulation Multiconference, SpringSim '09, 16:9, 16:1 pages, San Diego, CA, USA, March 2009.
- F. Lamperti, A. Roventini, and A. Sani, “Agent-based model calibration using machine learning surrogates,” Journal of Economic Dynamics & Control, vol. 90, pp. 366–389, 2018.
- M. Guerini and A. Moneta, “A method for agent-based models validation,” Journal of Economic Dynamics & Control, vol. 82, pp. 125–141, 2017.
- C. Gräbner, C. S. E. Bale, B. A. Furtado et al., “The best of both worlds: developing complementary Equation-Based and Agent-Based Models,” Computational Economics, p. 25, 2017.
- D. J. Watts and S. H. Strogatz, “Collective dynamics of 'small-world' networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998.
- A. Barabasi and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999.
- A. Clauset, C. Moore, and M. E. J. Newman, “Hierarchical structure and the prediction of missing links in networks,” Nature, vol. 453, no. 7191, pp. 98–101, 2008.
- A. E. Motter and M. Timme, “Antagonistic Phenomena in Network Dynamics,” Annual Review of Condensed Matter Physics, vol. 9, pp. 463–484, 2018.
- S. Battiston, D. Delli Gatti, M. Gallegati, B. Greenwald, and J. E. Stiglitz, “Credit chains and bankruptcy propagation in production networks,” Journal of Economic Dynamics and Control (JEDC), vol. 31, no. 6, pp. 2061–2084, 2007.
- S. Battiston, M. Puliga, R. Kaushik, P. Tasca, and G. Caldarelli, “DebtRank: too central to fail? financial networks, the FED and systemic risk,” Scientific Reports, vol. 2, article 541, 2012.
- S. Thurner and S. Poledna, “DebtRank-transparency: Controlling systemic risk in financial networks,” Scientific Reports, vol. 3, p. 1888, 2013.
- S. Poledna, S. Thurner, J. D. Farmer, and J. Geanakoplos, “Leverage-induced systemic risk under Basle II and other credit risk policies,” Journal of Banking & Finance, vol. 42, no. 1, pp. 199–212, 2014.
- K. Gray and A. E. Motter, “Multidisciplinary complex systems research,” Report from an NSF Workshop, 2017.
- T. P. Peixoto, “Nonparametric weighted stochastic block models,” Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol. 97, no. 1, p. 012306, 2018.
- J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, vol. 1, Springer series in statistics New York, New York, NY, USA, 2001.
- T. Hastie, R. Tibshirani, and J. Friedman, Elements of Statistical Learning: data mining, inference, and prediction, Springer, 2nd edition, 2009.
- I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, vol. 1, MIT Press, Cambridge, Mass, USA, 2016.
- S. Haykin and N. Network, “A comprehensive foundation,” Neural networks, vol. 2, no. 2004, p. 41, 2004.
- M. Abadi, P. Barham, J. Chen et al., “TensorFlow: A system for large-scale machine learning,” in OSDI, vol. 16, pp. 265–283, 2016.
- A. A. Boxwala, K. Jihoon, J. M. Grillo, and L. Ohno-Machado, “Using statistical and machine learning to help institutions detect suspicious access to electronic health records,” Journal of the American Medical Informatics Association, vol. 18, no. 4, pp. 498–505, 2011.
- W. Zhou and G. Kapoor, “Detecting evolutionary financial statement fraud,” Decision Support Systems, vol. 50, no. 3, pp. 570–575, 2011.
- F. Amato, A. López, E. M. Peña-Méndez, P. Vaňhara, A. Hampl, and J. Havel, “Artificial neural networks in medical diagnosis,” Journal of Applied Biomedicine, vol. 11, no. 2, pp. 47–58, 2013.
- R. Aggarwal, J. Ward, I. Balasundaram, P. Sains, T. Athanasiou, and A. Darzi, “Proving the effectiveness of virtual reality simulation for training in laparoscopic surgery,” Annals of Surgery, vol. 246, no. 5, pp. 771–779, 2007.
- J. B. Greenblatt and S. Shaheen, “Automated Vehicles, On-Demand Mobility, and Environmental Impacts,” Current Sustainable/Renewable Energy Reports, vol. 2, no. 3, pp. 74–81, 2015.
- A. D. Thierer, A. Castillo, and R. Russell, Artificial Intelligence and Public Policy, George Mason University, VA: Mercatus Research, 2017.
- D. T. Brooks, B. Becker, and J. R. Marlatt, “Computer applications in particular industries: securities,” in Computers and the law, American Bar Association, Section of Science and Technology, 3rd edition, 1981.
- J. Grimmer, “We are all social scientists now: How big data, machine learning, and causal inference work together,” PS - Political Science and Politics, vol. 48, no. 1, pp. 80–83, 2014.
- R. A. McCain, Game theory and public policy, Edward Elgar Publishing Limited, 2009.
- A. G. Sanfey, “Social decision-making: Insights from game theory and neuroscience,” Science, vol. 318, no. 5850, pp. 598–602, 2007.
- E. Lleberman, C. Hauert, and M. A. Howak, “Evolutionary dynamics on graphs,” Nature, vol. 433, no. 7023, pp. 312–316, 2005.
- B. Edmonds and C. Gershenson, “Modelling complexity for policy: opportunities and challenges,” in Handbook on complexity and public policy, p. 205, 2015.
- J. W. Forrester, “Counterintuitive behavior of social systems,” Technological Forecasting & Social Change, vol. 3, no. C, pp. 1–22, 1971.
- R. L. Eberlein and K. J. Chichakly, “XMILE: A new standard for system dynamics,” System Dynamics Review, vol. 29, no. 3, pp. 188–195, 2013.
- H. V. D. Parunak, R. Savit, and R. L. Riolo, “Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide,” in Multi-Agent Systems and Agent-Based Simulation, vol. 1534 of Lecture Notes in Computer Science, pp. 10–25, Springer Berlin Heidelberg, Berlin, Heidelberg, 1998.
- M. Niazi and A. Hussain, “Agent-based computing from multi-agent systems to agent-based models: a visual survey,” Scientometrics, vol. 89, no. 2, pp. 479–499, 2011.
- J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcation of Vector Fields, vol. 42, Springer Science & Business Media, 2013.
- N. Gilbert and K. Troitzsch, Simulation for the social scientist, McGraw-Hill Education, UK, 2005.
- A. T. Crooks and A. J. Heppenstall, “Introduction to agent-based modelling,” Agent-Based Models of Geographical Systems, pp. 85–105, 2012.
- M. Fujita, P. Krugman, and A. J. Venables, The spatial economy: cities, regions and international trade, MIT Press, Cambridge, Mass, USA, 1999.
- T. Bovaird, “Emergent strategic management and planning mechanisms in complex adaptive system,” Public Management Review, vol. 10, no. 3, pp. 319–340, 2008.
- R. Mahon, P. McConney, and R. N. Roy, “Governing fisheries as complex adaptive systems,” Marine Policy, vol. 32, no. 1, pp. 104–112, 2008.
- J. W. Hall, M. Tran, A. J. Hickford, and R. J. Nicholls, The future of national infrastructure: A system-of-systems approach, Cambridge University Press, 2016.
- ITRC-MISTRAL, “Multi-scale infrastructure systems analytics,” The UK Infrastructure Transitions Research Consortium, 2016.
- J. Hall, A. Otto, A. J. Hickford, R. J. Nicholls, and M. Tran, “A framework for analysing the long-term performance of interdependent infrastructure systems,” The Future of National Infrastructure: A System-of-Systems Approach, p. 12, 2016.
- J. W. Hall, J. J. Henriques, A. J. Hickford et al., “Assessing the Long-Term Performance of Cross-Sectoral Strategies for National Infrastructure,” Journal of Infrastructure Systems, vol. 20, no. 3, p. 04014014, 2014.
- E. A. Byers, J. W. Hall, and J. M. Amezaga, “Electricity generation and cooling water use: UK pathways to 2050,” Global Environmental Change, vol. 25, no. 1, pp. 16–30, 2014.
- G. Dosi and A. Roventini, “Agent-Based Macroeconomics and Classical Political Economy: Some Italian Roots,” Italian Economic Journal, vol. 3, no. 3, pp. 261–283, 2017.
- G. Dosi, M. Napoletano, A. Roventini, and T. Treibich, “Micro and macro policies in the Keynes+Schumpeter evolutionary models,” Journal of Evolutionary Economics, vol. 27, no. 1, pp. 63–90, 2017.
- F. Lamperti, G. Dosi, M. Napoletano, A. Roventini, and A. Sapio, “Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model,” Ecological Economics, vol. 150, pp. 315–339, 2018.
- M. Napoletano, G. Dosi, G. Fagiolo, and A. Roventini, “Wage formation, investment behavior and growth regimes: An agent-based analysis,” Revue de l'OFCE, vol. 124, no. 5, pp. 235–261, 2012.
- G. Dosi, G. Fagiolo, M. Napoletano, and A. Roventini, “Income distribution, credit and fiscal policies in an agent-based Keynesian model,” Journal of Economic Dynamics & Control, vol. 37, no. 8, pp. 1598–1625, 2013.
- G. Dosi, G. Fagiolo, M. Napoletano, A. Roventini, and T. Treibich, “Fiscal and monetary policies in complex evolving economies,” Journal of Economic Dynamics & Control, vol. 52, pp. 166–189, 2015.
- T. Balint, F. Lamperti, A. Mandel, M. Napoletano, A. Roventini, and A. Sapio, “Complexity and the economics of climate change: a survey and a look forward,” Ecological Economics, vol. 138, pp. 252–265, 2017.
- G. Dosi, M. C. Pereira, A. Roventini, and M. E. Virgillito, “Causes and consequences of hysteresis: aggregate demand, productivity, and employment,” Industrial and Corporate Change, vol. 27, no. 6, pp. 1015–1044, 2018.
- G. Dosi, M. C. Pereira, A. Roventini, and M. E. Virgillito, “When more flexibility yields more fragility: the microfoundations of Keynesian aggregate unemployment,” Journal of Economic Dynamics & Control, vol. 81, pp. 162–186, 2017.
- F. Jaumotte and C. Osorio-Buitron, “Inequality and Labor Market Institutions,” IMF Staff Discussion Note, p. 31, 2015.
- P. Waddell, G. F. Ulfarsson, J. P. Franklin, and J. Lobb, “Incorporating land use in metropolitan transportation planning,” Transportation Research Part A: Policy and Practice, vol. 41, no. 5, pp. 382–410, 2007.
- P. Waddell, “A behavioral simulation model for metropolitan policy analysis and planning: Residential location and housing market components of UrbanSim,” Environment and Planning B: Planning and Design, vol. 27, no. 2, pp. 247–263, 2000.
- J. K. Brueckner, “The structure of urban equilibria: A unified treatment of the muth-mills model,” in Handbook of Regional and Urban Economics, vol. 2, pp. 821–845, Elsevier Science Publishers B.V., 1987.
- S. Rosen, “Hedonic prices and implicit markets: product differentiation in pure competition,” Journal of Political Economy, vol. 82, no. 1, pp. 34–55, 1974.
- P. Waddell, “Integrated land use and transportation planning and modelling: Addressing challenges in research and practice,” Transport Reviews, vol. 31, no. 2, pp. 209–229, 2011.
- P. Waddell, L. Wang, and X. Liu, “UrbanSim: an evolving planning support system for evolving communities,” in Planning support systems for cities and regions, pp. 103–138, Lincoln Institute for Land Policy, Cambridge, MASS, USA, 2008.
- P. Waddell, L. Wang, and B. Charlton, “Integration of a parcel-level land use model and an activity-based travel model,” in Proceedings of the 11th World Conference on Transport Research, 2007.
- P. Waddell, C. R. Bhat, N. Eluru, L. Wang, and R. M. Pendyala, “Modeling interdependence in household residence and workplace choices,” Transportation Research Record, vol. 2003, no. 1, pp. 84–92, 2007.
- A. De Palma, K. Motamedi, N. Picard, and P. Waddell, “Accessibility and environmental quality: inequality in the Paris housing market,” European Transport, vol. 36, pp. 47–64, 2007.
Copyright © 2019 Bernardo A. Furtado et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.