Urban mobility as a product of an evolutionary system

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(1)URBAN MOBILITY AS A PRODUCT OF AN EVOLUTIONARY SYSTEM Master’s thesis Industrial Engineering. Juliana Gómez-Quintero. Camilo Olaya Thesis advisor. Universidad de los Andes Faculty of Engineering Department of Industrial Engineering Bogotá, 2011.

(2) Urban mobility as a product of an evolutionary system Juliana Gómez Quintero. Content 1.. Introduction ....................................................................................................................... 1. 2.. The urban mobility problem ............................................................................................. 1. 3.. 4.. 2.1. The concept of urban mobility ................................................................................... 1. 2.2. How is it measured? ................................................................................................... 1. 2.3. The problem ...............................................................................................................2. 2.4. Consequences and problem solving alternatives ..................................................... 4. 2.5. The effects of policies .................................................................................................5. Some concepts that underlie the current urban mobility framework .............................5 3.1. Cause-effect ................................................................................................................5. 3.2. Top-down implementation ....................................................................................... 9. 3.3. Efficiency over effectiveness .................................................................................... 10. 3.4. Homogeneity of individuals ..................................................................................... 10. 3.5. Reframing the problem: A different approach ........................................................ 11. An evolutionary perspective ............................................................................................ 11 4.1. A general framework to explain processes .............................................................. 11. 4.2. Evolution as a knowledge process ........................................................................... 12. 4.3. The logic of a selectionist process: Variation, selection and retention .................. 12. 4.4. Evolutionary theory in the context of social systems .............................................. 13. 4.4.1. 5. Micro, meso and macro domains ..................................................................... 14. 4.5. Policy design under an evolutionary perspective .................................................... 16. 4.6. Assumptions that underlie evolutionary thinking .................................................. 18. Urban mobility as a product of an evolutionary system................................................. 18 5.1. The concept of urban mobility ................................................................................. 18. 5.2. Description of the system ......................................................................................... 19. 5.3. Agent-Based Simulation model ............................................................................... 19. 5.3.1. Why build an Agent-Based simulation model? ............................................... 19. 5.3.2. Description of the model .................................................................................. 19 2.

(3) 5.3.3. Micro-meso-macro levels ................................................................................ 23. 5.3.4. An initial simulation to understand rule dynamics ........................................ 24. 5.4. Policy implementation: a general analysis ............................................................. 26. 6 Final remarks and discussion: The evolutionary system perspective vs. traditional approaches ............................................................................................................................... 31 References .............................................................................................................................. 33 Annex 1. Netlogo code of simulation model .......................................................................... 38. 3.

(4) Urban mobility as a product of an evolutionary system Juliana Gómez Quintero. 1. Introduction Urban mobility has become a principal concern for most cities around the world. Several policies have been implemented by planners hoping to mitigate the immobility problem. However, many of these policies have not produced the desired effect in cities‟ mobility conditions. Furthermore, urban populations will continue to grow, more people will need to move around a city and space for mobility will continue to be a scarce resource; hence, tackling the urban mobility problem successfully requires effective policies that mitigate the immobility problem. This project focuses on understanding urban mobility as a product of an evolutionary system. The aim is to offer an alternative way of approaching the situation such that it that permits the formulation of policies that generate a positive impact on the urban immobility problem. The advantages and implications of understanding the situation from this perspective are also evaluated. In the second section, the problem of urban mobility is described and analyzed in terms of the policies planners have implemented to mitigate the problem. In the third section some of the concepts that underlie the current policy formulation framework are identified and evaluated in terms of its correspondence with the urban mobility system‟s characteristic. In section four, an evolutionary perspective is introduced as an alternative way of framing the problem. In section five, urban mobility is described under an evolutionary framework. A simulation model was built to illustrate urban mobility dynamics under the evolutionary perspective; three policies are implemented in the simulation model. In the last section, the advantages and implications for policy formulation are discussed under an evolutionary framework along with suggestions for further research. This work emphasizes the importance of the assumptions that guide a way of thought and, analyses both assumptions and implications that undermine traditional approaches, as well as those related with the evolutionary system perspective.. 2. The urban mobility problem 2.1 The concept of urban mobility Urban mobility, as defined by Hanson and Giuliano (2004) is “the ability to move between different activity sites”. Similarly, Michael Meyer (1997) defines it as the “ability and knowledge to travel from one location to another using a multimodal approach”. Thus, urban mobility is concerned with the mobilization of people.. 2.2 How is it measured? One of the ways of measuring mobility is, as suggested by Norwood and Casey (2002), in terms of “the time and costs required for travel”. In a similar sense, Vasconcellos associates urban mobility with the number of daily trips per person (Alcantara de Vasconcellos, 1.

(5) 2005). In the report presented by the Observatorio de Movilidad Urbana (Urban Mobility Observatory) supported by CAF (Corporación Andina de Fomento) mobility was defined as an index of trips/person/day (CAF, 2010) Another form of measuring urban mobility can be taken from Priemus, Njikamp and Banister‟s (2001) explanation of the concept which “involves the transport of both passenger and freights”. They also describe urban mobility as a “derived demand” (Priemus, Nijkamp, & Banister, 2001). This suggests urban mobility can also be measured in terms of the volume of people that need or want to travel. Likewise, Acevedo et al. refer to the concept in terms of demand for mobility (Acevedo, Bocarejo, Lleras, & Rodríguez, 2009). Schafer and Victor also use the urban mobility concept to “denote traffic volume” (Schafer & Victor, 2000). In this sense, urban mobility could be understood as “a matter of transportation services provision” (Silva, Costa, & Macedo, 2008). Similarly, Vukan Vuchic states urban mobility is measured by “vehicles-km or person-km traveled” (Vuchic, 2000).. 2.3 The problem. Figure 1 Inmobility: a worldwide problem. However, cities around the world are facing an immobility problem. In Beijing, the average speed of vehicles declined from 45 km/h in 1994 to 10km/h in 2005 (World Bank, 2006). São Paulo mobility reports show automobile speeds in the main arterial system dropped to less than 20km/hr during the 1990‟s compared to speeds between 27 km/h and 28 km/h 2.

(6) in the late 1970‟s (Engenharia de Tráfego e de Transportes Ltda, 2009). Moreover, in Bogota, studies show a decrease in the average traveling speeds of vehicles in the main roads; considering the projected growth of vehicles for the city, by the year 2012, the average speed per vehicle in one of the main city roads would be of 7 km/h. (Acevedo, Bocarejo, Lleras, & Rodríguez, 2009). Likewise, London and cities in the United States serve as examples of world‟s urban mobility problem. The 2010 mobility report for London “Transport for London” remarks that “despite falling travel levels this decade, road traffic congestion has been increasing in all areas of London for some years. Over the period from 1977 to the last complete survey cycle in 2003 to 2006, average weekday Greater London main road traffic speeds fell by 14 percent in the morning peak period (…) by 12 percent during the mid-day inter-peak period (…) and by 9 percent in the weekday evening peak period, to 25.6 kilometers per hour” (Transport for London 2010, 2010). In the United States, the 2010 report about mobility (Texas Transportation Institute, 2010) shows a decrease in congestion levels in most cities but highlights this is associated with the current economic recession and that “congestion problems will return when the economy begins to grow” (Texas Transportation Institute, 2010) based on the premise that “economic recessions cause fewer goods to be purchased, job losses mean fewer people on the road in rush hours and tight family budgets mean different travel decisions are made” (Texas Transportation Institute, 2010). This report also mentions that “travelers and freight shippers must plan around traffic jams for more of their trips, in more hours of the day and in more cities, towns and rural areas than in 1982” (Texas Transportation Institute, 2010). Even more, costs of congestion for the country, in terms of time wasted and fuel used, were estimated at 115 billion dollars for the year 2009 whereas in 2000 these were 85 billion dollars and 24 billion dollars in 19821 (Texas Transportation Institute, 2010). As Gakenheimer (1999) mentions: “Congestion is reducing the mobility of auto users”. In addition to congestion levels, “in developing cities, deteriorating urban transport disproportionately affects poor people, who tend to live either on the sprawling city periphery or in harsh inner-city areas, „with very poor access by the only mode of transport available to them, walking, non-motorized or public transport‟ (Gwilliam, 2003, p. 201)” (Santos, Behrendt, & Teytelboym, 2010). Moreover, Montezuma mentions that the most complex problems are related with low quality of the public transportation system, high levels of urban congestion and high fatality rates (Montezuma, Alternativas en movilidad urbana, 2007). With respect to the quality of the public transport system, Rodríguez Valencia states that the collective public transport operating inefficiency implies that the urban congestion problem cannot be exclusively attributed to the use of private vehicles. (Rodríguez Valencia, 2009). Notwithstanding, although the immobility problem is widespread, “environmental pollution, noise, traffic fatalities and injuries, congestion, and mobility problems are far more severe in developing countries, making the problems in Europe and North America seem quite modest by comparison” (Puchera, Korattyswaropama, Mittala, & Ittyerahb, 1. All values in constant 2009 dollars. 3.

(7) 2005). Furthermore, Putcher (2005) identifies the following factors as part of the immobility problem particularly in large cities of developing countries: limited and poor quality of the road network; rapidly increasing ownership and use of private cars and motorcycles; inadequate roadway accommodations for buses and non-motorized transport; extremely high and rapidly rising traffic fatalities, inefficient, unreliable and poor quality public transport; extremely high levels of transport-related pollution, noise and other environmental impacts.. 2.4 Consequences and problem solving alternatives The problem of immobility not only involves the movement of people from one location to another because transport is not a closed self-contained system, it is intimately related with other social systems (Goldman & Gorham, 2006). Santos et. Al (2010) for example, mention four dimensions of urban mobility: economic, social, environmental and health. To illustrate the possible impact of limited urban mobility in a city, Acevedo et al. explain that in a city with low opportunities for mobilization, productivity is constrained; this leads to a high possibility for the city to fall in a poverty cycle which may be very difficult to overcome (Acevedo, Bocarejo, Lleras, & Rodríguez, 2009). There is a wide range of policies that can be implemented to alleviate the urban mobility problem. Some of these include: land-use policies, incentives to walking and cycling, road construction, tele-commuting, car sharing, internalizing vehicle use costs, and restriction of vehicle use (Santos, Behrendt, & Teytelboym, 2010) . Tennoy (2010) classifies the most commonly implemented policies into four main groups: 1. Encouraging land-use development that demands less transport and less car use, 2. Imposing physical and fiscal restrictions on car traffic, 3. Improving public transport services, and 4. Improving conditions for walking and bicycling. In addition to these groups a fifth group is proposed to include policies that involve improving the infrastructure available for transport such as increasing the road network. In addition to these policies and considering the environmental side-effects of urban mobility, the concept sustainability becomes an important element in the context of urban mobility. Acevedo and Bocarejo affirm that the studies and mobility plans do not constrain themselves to the development of transportation systems that minimize costs and time of travelling of persons and freight but they also analyze their contribution to social development, rational use of scarce resources and impact on the environment (Acevedo & Bocarejo, Movilidad sostenible: Una construcción multidisciplinaria, 2009). Carmen Lizarraga Mollinedo writes urban mobility should be defined around the existence of a system and transportation patterns that are capable of supplying the means and opportunities to cover the economic, environmental and social needs in an efficient and equitable manner such that it avoids the unnecessary negative impacts and associated costs. (Lizárraga Mollinedo, 2006). Lizarraga also suggests that a sustainable urban mobility strategy should be integrated in a globally sustainable system that permits the coverage of the current needs without compromising the possibility for future generations to satisfy them (Lizárraga Mollinedo, 2006).. 4.

(8) 2.5 The effects of policies Nevertheless, Goldman and Gorham suggest that mobility projects usually ignore “the accessibility and productive systems in which transportation sits, and from which it derives its economic value. Instead, it focuses on one component – „mobility‟ – and gives it primacy over other possible components of a transportation/accessibility/productivity system” (Goldman & Gorham, 2006). The result is that sustainable policies “those that try to reduce mobility – are off-limits” (Goldman & Gorham, 2006). However, reducing growth in urban road traffic volumes is still a main objective for several cities, countries and international institutions (Tennoy, 2010). Regardless, Tennoy (2010) mentions that in spite of the efforts to mitigate urban congestion and, therefore, improve urban mobility “cities continue to be planned and developed in ways that cause and allow growth in urban road traffic volumes” (Tennoy, 2010). Hence, a deeper analysis is required to understand the implemented policies‟ insufficiency to lessen the immobility problem. Tennoy (2010) explains that “how the problem is framed influences the choice of alternative strategies and means that are considered, how these are evaluated, what counts as evidence, what are seen as legitimate methods, and thus the alternatives or means that are recommended and implemented…”. Thereby, the starting point of this work will be a review of four concepts that underlie some of the policies formulated for the urban mobility described in this section.. 3. Some concepts that underlie the current urban mobility framework 3.1 Cause-effect Cause-effect reasoning is reflected upon an “event-oriented approach to problem solving” (Sterman, 2000). As suggested by Sterman (2000), policies are often formulated based on cause-effect logic (Figure 2). Planners define the problem as the difference between the situation they perceive and their desired situation (their goals). Consequently, a set of possible solutions to the problem are evaluated and the alternative considered to be the best is selected and implemented; after implementation favorable results are expected. Goal s Proble m. Decisi on. Result s. Situati on Figure 2 Cause-effect logic for problem solving (Sterman, 2000). Sterman (2000) affirms cause-effect reasoning ignores the existing feedback between the decisions implemented and the situation; furthermore, cause-effect logic ignores the fact that there may be other agents with other goals that can decide and act influencing the 5.

(9) environment producing unexpected side effects. Hence, the author suggests a “feedback view” of the world that incorporates these elements; figure 3 shows such view. In addition to the cause-effect logic shown in figure 2, implementing the decision chosen to “solve” the problem, affects the observed situation which also determines a new set of goals. Implementing the decision will also generate actions of other agents in the system (e.g citizens), who act according to their goals, the situation (environment) and the implemented decision. Actions of other agents, in turn, affect the observed situation.. Figure 3 "Feedback view" for problem solving. Based on Sterman (2000). Cause-effect reasoning in the urban mobility framework Cause-effect reasoning can be identified in the way some policies have been formulated in the context of urban mobility. Two examples of these policies are vehicle restriction and improvement of the available infrastructure for transport. Vehicle restriction policies Pico y placa “Pico y placa” is a measure that has been adopted in several Colombian cities such as Bogota as well as in cities in Venezuela and Mexico. In Bogota, it was implemented in 1998 with the purpose of reducing vehicle flow during peak hours in order to alleviate congestion generated and stimulate vehicle users to transport themselves at other hours (Alcaldía Mayor de Santa Fe de Bogotá, 1998). From Monday to Friday, cars with the last digit of their license plate included in a particular set of four digits had a circulation restriction during peak hours (between 7 am and 9 am, and between 5:30 pm and 7:30 pm); every car had the restriction two days per week. This meant that 40% of the vehicles were banned from circulating during peak-hours every week day. The cause-effect logic for the formulation of this policy (Figure 4) is best illustrated by the motivations that led to the Pico y Placa measure applied in Bogota. As stated in the mayoral decree 626 of 1998 (Alcaldía de Bogotá,, 1998) some of the motivations for the implementation of this measure were:. 6.

(10) 1) The existence of high traffic volumes in this city that caused congestion and impeded an adequate vehicle transit during labor days. 2) That the previous efforts of the district administration to improve vehicle transit during these hours such as counter flows on main roads and restriction of freight carrier vehicles during this time period were insufficient given the increase in the traffic levels. 3) That the mayor has the responsibility, as a transit authority, of taking the necessary measures to overcome road congestion and guarantee a satisfactory traffic level according to adequate safety margins required to maintain public order. Motivation 1), identifies the problem as high levels of congestion; motivation 2) evidences there has been a previous trial of other policies that have resulted “insufficient” to lessen the immobility problem; and finally, motivation 3) suggest the implementation of the vehicle restriction policy is considered a necessary measure that will guarantee a satisfactory traffic level.. Accepted congestion level Too many cars on streets. Vehicle circulation restriction. Less cars on streets. Street congestion Figure 4 Cause-effect logic for the formulation of vehicle restriction policies.. After implementing Pico y Placa in Bogota, the number of private vehicles kept on growing (Bogotá cómo vamos, 2008) as well as the number of citizens that claimed to have larger travel times than in previous years (Bogotá cómo vamos, 2009). As for 2008, mobility reports showed an increase of 55% in the quantity of registered vehicles since 2002 (Bogotá cómo vamos, 2008). Similarly, projections of the motorization rates of the city up to 2040 indicate an average yearly growth of 23%2 (Acevedo, Bocarejo, Lleras, & Rodríguez, 2009). The reason why this measure did not bring about the desired effects is often attributed to the fact that in spite of the existing vehicle congestion, it is generally more advantageous to use a private vehicle than public transport. Consequently, and given the low market prices of vehicles, citizens with enough economic resources to do so prefer to buy another vehicle to avoid the restriction (Bogotá cómo vamos, 2008). Hence, regardless of the decrease in traffic levels due to less vehicles circulating per day, it is just a matter of time (the time required for individuals to buy new vehicles) for the traffic levels to reach and outrun the actual levels. This “side-effect” of the policy was not considered by planners; a “feedback” 2. Calculations based on the data provided in (Acevedo, Bocarejo, Lleras, & Rodríguez, 2009).. 7.

(11) view of the policy and its side-effects is shown. Rodríguez Valencia mentions that these measures are considered palliative and dilatory given that they postpone the moment of confronting the problem. (Rodríguez Valencia, 2009). A feedback view of this problem is shown in figure 5.. Improving. Figure 5 "Feedback view" on Pico y placa and its sidetheeffects. available infrastructure for transport. Increasing the road-network: road construction Road construction can be thought as a measure to enhance urban mobility given that it provides more space for circulation; this reasoning suggests cause-effect logic (Figure 6).. Accepted congestion level Insufficient space for vehicles to circulate. Road construction. Less cars per street. Street congestion. Figure 6 Cause-effect logic for the formulation of improvement of the available infrastructure for transport policies.. However, as Salas Rondon mentions, the construction of new roads produces and attracts demand for travel (Salas Rondón, 2009). Hence, “if new roads increase the volume of traffic, congestion relief will be less than anticipated, or shorter in duration, than if there is no such extra traffic” (Goodwin & Noland, 2002)) (see Figure. There are several statistic studies that evaluate the relationship between increase in road capacity and the volume of traffic. The traffic volume is measured in terms of vehicle miles of travel (VMT). Noland‟s calculations about the induced traffic due to increase in road capacity illustrate the positive relationship between road capacity and VMT: 28% of VMT is attributed to road capacity increases (Noland, 2001). A feedback view of this problem is illustrated in figure 7.. 8.

(12) Figure 7 "Feedback view" on road construction policy and its side-effects.. 3.2 Top-down implementation Top-down policy implementation is based on the assumption “that implementation begins with policy or legislative objectives, and that the processes of implementation will follow on in a fairly linear fashion from this” (Schofield, 2001) As such, top-down implemented policies are do not consider the participation of citizens and assuming that once the government implements the policy, individuals will accept and respond in a satisfactory manner. However, “top-down models do not deal very well with the messiness of policy making, behavioral complexity, goal ambiguity and contradiction” (Schofield, 2001) and hence, the policies implemented do not render the expected results. Examples of this type of policy implementation may be found in the context of urban mobility. As Preston asserts, top-down, mechanistic planning solutions need to be supplemented by bottom-up community participation. This would enable a detailed assessment of normatively assessed needs against expressed wants (Preston & Rajé, 2007). Top-down implementation in the urban mobility framework Some public transport related policies (e.g Transantiago - integrated transit system in Santiago, Chile) are examples of policies that have followed, at least in part, a top-down implementation perspective. An example of public transport related policies Transantiago Transantiago is the integrated transit system in Santiago, Chile. It was implemented in February 2007 and “was designed to provide a modern, integrated, and sustainable public transport system for the entire city” (Muñoz & Gschwender, 2008). However, when the system was implemented, Santiago‟s citizens did not understand how the system worked as well as the routes that the buses followed and hence people overflowed the already known metro system. In fact, the metro system reached occupancy factors of about 6 passengers/m2 (Muñoz & Gschwender, 2008). Furthermore, Muñoz and Gschwender (2008) write that to date, “… the government, city residents and especially the users of the 9.

(13) system are far from convinced of its benefits. Indeed, many “Santiaguinos” consider it to be a complete failure”. Part of Transantiago‟s initial failure is due to its top-down implementation. In fact, authors that participated in the planning process remark some of the mistakes in the project‟s implementation were the almost inexistent public participation and the sudden implementation of the system from one day to another (Muñoz & Gschwender, 2008).. 3.3 Efficiency over effectiveness Efficiency is defined as the competence to obtain the desired results (Court & Hill, 1995). On the other hand, effectiveness is defined as producing the desired effect (Court & Hill, 1995). In other words, efficiency is how resources are utilized to produce a desired result whilst effectiveness is achieving the desired result. Kurt Dopfer identifies these two concepts as “basic abilities relevant for problem solving” in a social system (Dopfer K. , 2005). The efficiency and efficacy dilemma is a transversal issue to policy formulation. With respect to policy formulation in the urban mobility context, the World Bank mentions efforts are normally concentrated “on applying an efficiency criterion to each policy decision” (World Bank, 2002). However, even though efficiency is a necessary element in policy formulation given that resources are scarce, an ineffective policy that needs infinite resources is not viable and an efficient resource management might result to be futile if the desired result is not achieved. Hence, policies should focus not only on efficiency but on efficiency as well.. 3.4 Homogeneity of individuals Complex variety governs social cognition and social behavior (homo sapiens.. Dopfer). Ross and Punpuing (2001) suggest that traffic problems, and I add mobility problems, cannot be viewed “in terms of travel infrastructure and vehicle movements alone”. The author mentions that “the human behavior involved in travel to and from work (and for that matter, other travel) is important both in delineating the nature of the problems, and for understanding how people‟s choices and amendments of time, mode, distance and direction contribute to traffic patterns” (Punpuing & Ross, 2001). However, as Ross states, “there is ample evidence to illustrate the mismatch between urban transportation planning methods and the growing transportation problems” (Punpuing & Ross, 2001). Most studies in urban mobility, in most cases the raw material for policy formulation, are based on statistical or on economic models where users have a uniform utility function; common tools for analysis are regression models, neoclassic economic models and simulation models such as Tranus and Visum. These models provide a simulation platform that allows the design of transportation systems considering a fair amount of variables, however these are static simulators and allow for a limited number of utility functions for individuals. Nevertheless, there have been initiatives to include individuals‟ heterogeneity by generating multiple utility functions and decision making criteria in this context; examples include: (Hackney & Marchal, 2011), (Walker, Ehlers,. 10.

(14) Banerjee, & Dugundji, 2011) and (Cantos-Sánchez, Moner-Colonques, Sempere-Monerris, & Álvarez-SanJaime, 2011).. 3.5 Reframing the problem: A different approach The urban mobility problem poses challenges for governments around the world to formulate effective policies. Even for cities that have managed to cope with the problem, urban population growth is bound to keep increasing; therefore, planners should consider the formulation of policies that will allow satisfactory mobility conditions in cities with large populations but limited space. Several of the policies that have been formulated in the context of urban mobility are framed in cause-effect logic, top-down implementation approach, search of efficiency instead of effectiveness and derive from studies that assume uniformity of individuals. These concepts lead to the formulation of policies that do not necessarily comply with reality and as a consequence have not brought the expected outcomes. One can then question if the way that the problem of urban mobility has been understood and intervened is the most appropriate. How to formulate policies that may have a favorable impact? Could there be a different concept of urban mobility that facilitates a better comprehension of the problematic and that leads towards more appropriate policies that can solve or mitigate the problem? As Tennoy (2010) writes: “…planners need to do planning differently in the future. How objectives, ends and values are emphasized and ranked need to change, as well as knowledge, understandings and theories.” Consequently, the rest of this work will introduce an alternative way of framing the urban mobility problem in the hope of formulating more effective and reality coherent policies.. 4. An evolutionary perspective An alternative way of framing the urban mobility problem is under an evolutionary perspective. In the following sections, such perspective is described along with relevant concepts that allow the further framing of the problem under this perspective and the creation of a framework for policy formulation.. 4.1 A general framework to explain processes Selectionism has its origins in the Theory of Natural Selection proposed by Charles Darwin to explain the evolution of organisms. However, although it originates from biology, this evolutionary theory does not pretend to explain biological processes; instead, it “…provides a general, meta-theoretical framework for dealing with complex evolving systems, consisting of populations of varied and replicating entities, which are found in both nature and human society” (Hodgson & Knudsen, 2008). Therefore, the evolutionary perspective takes the selectionist view to explain social or economic processes recognizing the ontological communalities “at a high level of abstraction and not at the level of detail. This communality is captured by concepts such as replication and selection, which are defined as precisely and meaningfully but in a high general and abstract sense” (Aldrich, Hodgson,. 11.

(15) Hull, & et, 2008). This means, this evolutionary theory can be used as a general framework to explain, not only biological, but other kinds of processes.. 4.2 Evolution as a knowledge process As Rescher mentions: “Evolution, be it of organism or of mind, of subatomic matter or of the cosmos as a whole, reflects the pervasive role of process which philosophers of this school see as central both to the nature of our world and to the terms in which it must be understood. Change pervades nature. The passage of time leaves neither individuals nor types (species) of things statically invariant. Process at once destabilizes the world and is the cutting-edge of advance to novelty.” (Rescher N. , 2008) Accordingly, Bartley defines evolution as “…a process in which information regarding the environment is literally incorporated, incarnated, in surviving organisms through the process of adaptation” (Bartley, 1987, p. 23). Campbell states “evolution - even in its biological aspects – is a knowledge process” (Campbell, 1987).. 4.3 The logic of a selectionist process: Variation, selection and retention Evolutionary theory, based on a selectionist logic, focuses on the process of continuous change that shapes a product in an environment (Brownlee, 2007). Selectionism is a perspective in which adaptation is understood as the product of selection from among variations (Brownlee, 2007). This perspective requires a source of variation, a selectionist process and a mechanism that allows propagation and/or conservation of the selected units. The general logic can be explained as a process of trial and error in which: “variation is generated, selected, and maintained/propagated through evolutionary cycles” (Olaya, 2008). To illustrate better the variation and selection logic, two examples of its application are explained: natural selection theory and neuronal selection theory. Natural selection In natural selection, species and individuals compete for survival in the environment. Within a species, individuals have similar but different phenotypes; these phenotypes are a physical representation of their genotypes (genetic code). The source of variation is the different phenotypes, but what is selected is the genetic coding of the individual (Brownlee, 2007). As Brownlee (2007) explains it, the process of natural selection can be described as: “An inherited variation (born with, rather than acquired over the organisms‟ lifetime) that results in a slight increase in chance of survival of the organism in its local environment (and subsequent reproduction) is a benefit, thus the organism is naturally selected in the face of competition”. Successful variations are conserved by the species through sexual reproduction and inheritance of such characteristics from parents to offspring.. 12.

(16) Neuronal selection Neuronal Selection Theory, proposed by Edelman, aims to explain brain function, specifically to illustrate how connections among neurons are organized to respond successfully to a stimulus from the environment. (Edelman, 1978) It suggests there is an initial variety of connections (synapses) between neurons which are selected in time according to their effectiveness of response to the stimuli from the environment. In this case, the source of variety is the connectivity within a group of neurons and the unit of selection is the synapses (Brownlee, 2007). Successful connections become stronger every time they receive a stimuli and are retained while unsuccessful connections are eliminated. In short, evolutionary theory is concerned with knowledge processes, its adaption and how this is a product of variation and selectionist processes.. 4.4 Evolutionary theory in the context of social systems Kurt Dopfer suggests a framework to understand social systems under an evolutionary perspective. In a system, an agent is seen as representing an information carrier, any information thus „carried‟ is called knowledge (Dopfer K. , 2005). Hence a social system is sustained by knowledge (1996, Tsoukas) and can be understood as a complex system of knowledge (Dopfer & Potts, 2004. ). In this section, the evolutionary ontology proposed by Kurt Dopfer is adapted in order to conceptualize a social system as evolving knowledge driven by continuous variation and selection processes. In this context, knowledge units are characterized as rules and hence a social system is understood as a complex and “emergent rule-system” (Dopfer & Potts, 2004. ), a “population of rules, a structure of rules, and a process of rules” (Dopfer, Foster, & Potts, 2004). Rules are understood as procedures that allow the resolution of a problem (Dopfer K. , 2005). These rules are created, carried and used by social agents who are understood as rule-maker and rule-user animals (Dopfer K. , 2005). The creation, selection and utilization of these rules explains the interaction of agents within a system, and thus understanding the evolution of these rules (knowledge) leads to understanding the behavior of a social system. In short, agents in a social system carry and use rules that allow them to solve a specific problem. These agents can create, select and abandon rules. The use, creation, abandonment and selection of these rules produce what it is observed as the behavior of the system. For selection to occur there must be an initial repertoire of variations (1989, Darden, The Nature of Selection). Rule variations provide this initial repertoire. Selection upon this variety then acts as an elimination process in which those rules which are less fit are eliminated and a definite group of fit rules are retained and actualized by the agent. Rules are determined by two levels of analysis: a generic level, which defines a general logic to solve a problem and an operant level, which relates to how an individual acquires and uses (operates) the rule. A population of rules is formed by rules with the same generic level but different actualizations (operant level). To illustrate this difference, consider the problem of moving in a city from an origin (O) to a destination (D). To solve the problem. 13.

(17) there can be a set of different rules (Rij) where the index i indicates the generic level and j the operant level. Consider the following generic levels: 1. Walking 2. Taking the bus 3. Driving a private vehicle In this case, for example, two agents (a and b) could adopt R1j rule. However, even though both agents would go walking from O to D, the chosen route, the velocity of walking, the number of stops made, among other elements would be different given that these depend on the particular preferences, knowledge and characteristics of the agent who is using the rule. These differences make up the operant level. Hence, agent a would be using rule R1α (α denoting the operant level of the rule) and agent b would be using rule R1β.However, R1α and R1β would make up the population of the generic rule R1j. The operant level helps explain the variety of ways in which a rule can be used given that each individual may interpret a rule differently according to his own characteristics and rationality. It illustrates the diversity of entities that can be found within a rule population and highlights the importance of considering heterogeneity of individuals in a social system rather than assuming all are and behave in a uniform way. As Mayr (Mayr, The Biology of Race and the Concept of Equality. , 2002) states: “…variation has reality, while the mean value is simply an abstraction. One must treat each individual on the basis of his or her own unique abilities, and not on the basis of the group's mean value”. Variation and selection act on the generic level of rules: “Generic cognition and generic behavior refer to the creation, selective adoption, adaptation and retention of problemsolving rules” (Dopfer K. , 2005). Given that the aim is to understand the behavior of a social system as the product of the creation, selection and utilization of these rules, focus of analysis is placed on this generic level rather than the operant level. To explain the evolutionary dynamics of rules, Dopfer suggests a three stage process: origination, adoption (selection) and retention (Dopfer K. , 2005) that can be observed in three domains: micro, meso and macro. 4.4.1 Micro, meso and macro domains Micro, meso and macro domains allow the observation of a social system from three different perspectives. Each domain provides specific information about the system in terms of individual and collective behaviors; observing a system from only one of these perspective results in a biased perception of the system‟s dynamics. Combining understanding of behavior in these three domains allows an integral view on how the system produces the observed behavior. The micro level “refers to the individual carriers of rules and the systems they organize” (Dopfer, Foster, & Potts, 2004). In this level, the center of attention is “how the economic agent carries and uses rules, with the complex systems of connections that result, and with the processes by which these change.” (Dopfer, Foster, & Potts, 2004). At the micro level, an agent may select a rule from a portfolio of rules that allow the resolution of a problem. 14.

(18) At this level, origination refers to the creation of new rules and represents the initial variation required for the process of selection to occur. Adoption refers to the “internal selection, learning and adaptation of rule in given generic knowledge base” (Dopfer K. , 2005) and retention refers to the mechanisms of conservation of the selected rules such as “memory, information retrieval and recurrent rule activation that manifest themselves behaviorally in habits and routines” (Dopfer K. , 2005). The meso level transcends the individual level and focuses on a population of a rule actualizations. The concern in this level is the characteristics of a population such as its development, growth rate and size (Dopfer, Foster, & Potts, 2004). In the meso level, origination is concerned with the creation of a new rule population, adoption with how this population grows as new individuals adopt the rule and retention with the stabilization of rule populations, the conservation of such rules at a collective level, for example through the institutionalization of a rule in an organization (Dopfer K. , 2005). Finally, the macro level is concerned with the dynamics among rule populations (instances of the meso-level). The macro level is a coordinated structure of meso-levels where rule populations are interrelated. Origination, is understood as a disruption in the macro structure; such disruption can be caused by a new rule population in the system. This disruption implies a de-coordination of the structure. After de-coordination, the structure needs to be re-coordinated, that is, create new associations among the existing populations. This process of re-coordination is the adoption phase. Last, in the retention phase, a new structure of interrelated rule populations is created, this structure is coordinated. In this phase, meta-stability is reached and patterns of self organization are observed (Dopfer K. , 2005). The term “meta-stability” refers to the persistent maintenance of particular orders despite the permanent challenge of variation and selection dynamics (Dopfer K. , 2005). Figure 8 illustrates the general micro-meso-macro schema described previously.. Figure 8 General micro-meso-macro schema. For better illustration, Table 1 summarizes this micro-meso-macro schema with the example of the OD problem: 15.

(19) Micro: Individuals with a rule portfolio. Meso: A population of rules. Variation. Rule origination: an agent creates a new rule, to use a new transport mode (e.g. bicycle). Origination of a rule population: more agents adopt the bicycle rule; hence a rule population is created.. Selection and retention. Rule selection, bicycling becomes a habit through the rule selection and recurring utilization of the rule.. Rule diffusion on a collective level: several agents use the rule of bicycling regularly.. Macro: A coordinated structure of several rule populations De-coordination: the new rule population disturbs the mobility system, its internal rule structure and other rule populations. Re-coordination and coordination: new relations are established within the rule system and a new coordinated structure emerges.. Table 1 Micro-meso-macro schema in the example of the OD problem. 4.5. Policy design under an evolutionary perspective. Dopfer‟s evolutionary ontology provides a framework to understand a social system from an evolutionary perspective. But how to coordinate different rule populations and/or how to modify individuals‟ routines in order to influence a social system‟s behavior? How to design effective policies? In the micro-meso-macro schema specific questions about rule dynamics arise; these are summarized in the following table: Micro Origination: What motivates an individual to innovate? Where does this innovation come from? Adoption: What makes an individual select a specific rule? Why and when are specific rules more successful than other ones? Retention: What makes an individual retain a rule? Meso Origination: How is a rule population created? What motivates the creation of a new rule population? Adoption: What motivates collective selection of rules? Why some rules are more rapidly replicated through the system? Retention: What motivates the retention of rules on a collective level? How do these become institutionalized routines? Macro Origination: How is a coordinated rule population structure disturbed? Adoption: How are new relations among rule population formed? How should populations of rules be coordinated to engineer mobility at the system-level? Retention: What leads to the coordination of rule populations? What patterns of self-organization emerge? Table 2 Questions that arise in each of the levels in the micro-meso-macro schema. In Dopfer‟s proposal, individuals are proactive, free decision makers; agents decide to create, use and retain rules. According to this ontology, behavior of a system is the product of the interaction among rule populations which, in turn, is the product of the different actualizations of a rule carried out by individuals. Hence, to influence behavior one should center the attention on rules and the way these change. Understanding the questions 16.

(20) suggested in table 2 facilitates effective policy design. For example, comprehending what motivates an individual to innovate and create new rules allows policy makers to anticipate (without predicting the outcome) the creation of new rules and hence designing policies that can cope with such variations. Furthermore, once it is understood what motivates an individual to select and retain a specific rule, policies can be designed to motivate such selection and further retention. On a collective level (meso level) understanding rule dynamics facilitates the formulation of policies that exploit collective synergies of diffusion and retention of rules; in this sense, policies are aided by this synergy and, hence, it is not necessary to design mechanisms to control each individual but, instead, motivate collective behavior. Finally, on a macro level, understanding the interrelationships among rule population allows the deeper understanding of how the system produces the observed behavior and how self-organization patterns emerge. Understanding how rule population structures reach meta-stability may facilitate the formulation of policies that design coordinated rule population structures that produce satisfactory self-organization patterns. Understanding what makes routines (rules that are retained on a collective level), facilitates analysis of rule dynamics on the meso level. Routine theories turn out to be particularly useful to understand the selection and retention of rules within social systems. As Becker explains, “in order to understand an organization and its behavior, analyzing its routines thus seems an appropriate starting point since they capture systematic and endogenous (rather than exogenous or one-off) performance drivers, and what can be considered typical for an organization.” (Becker & Zirpoli, 2008).Even though authors of these theories analyze organizational routines, rather than those related with social systems in general, these can be generalized carefully given that an organization is a social system. Routines may be divided into two parts: ostensive and performative. Feldman and Pentland define the ostensive part as the “abstract, narrative description”‟ (Feldman and Pentland, 2003: 95) and the performative part as “actual performances by specific people, at specific times, in specific places” (Feldman and Pentland, 2003: 95). The performative part of routines, then, characterizes individual‟s behavior produced when using the routine and hence, effective policies should eventually affect the performative aspects of routines. A common way of addressing this problem is through artifact design. Artifacts are “inanimate objects introduced by organizational members into their organizations” (Vilnai-Yavetz, I., & Rafaeli, A. (2006); these may be brochures, manuals, tools etc. Nevertheless, artifact design results unsuccessful in modifying the performative part of routines because it is assumed that designing an artifact implies it will be used, understood and translated to a specific mode of behavior (Pentland and Feldman, 2008 – Designing routines). Pentland and Feldman (2008) argue there is a frequent disconnect between goals and results “because people design artifacts when they want patterns of action”. These authors also state that “like the folly of rewarding one thing while hoping for another, (…) designing things while hoping for patterns of action is a mistake” (Pentland and Feldman, 2008 – Designing routines). Any routine that involves people, who are capable of learning from experience, is at least partially a „„live” routine. (Pentland and Feldman, 2008 – Designing routines). While artifacts may serve as a guide for action, the manner of use and interpretation leaves open 17.

(21) a lot of possibilities (D‟Adderio, 2008). (Pentland and Feldman, 2008 – Designing routines). Consequently, to generate change in behavior, careful analysis should be done on how to change rules on an individual and collective levels, from a bottom-up perspective, considering multiplicity of rule actualizations, rather than designing artifacts that are implemented uniformly and in a generic way, top-down perspective, with the purpose of successfully modifying behavior.. 4.6. Assumptions that underlie evolutionary thinking. Given that the way a problem is framed will influence which courses of action are taken and hence what policies are formulated (Tennoy, 2010), the assumptions that underlie this evolutionary framework are made explicit in this section. Dopfer‟s evolutionary ontology assumes proactive, problem-solving free decision maker individuals. Given that individuals require solving a specific problem, the creation, selection and retention processes of rules driven by effectiveness (if the rule does not solve the problem it is not considered for selection) and efficiency (given a portfolio of effective problem-solving rules, these are selected according to the most convenient for the individual) criterion. However, individuals are heterogeneous and do not make decisions in a uniform manner and therefore their efficiency criteria is not the same. Behavior is produced by the decisions made by agents through rule variation and selection processes and hence, behavior is an endogenous product of the interactions within a rule system. Moreover, understanding behavior as an endogenous product of a specific rule system implies context specificity needs to be considered when formulating policies. Dopfer‟s evolutionary ontology assumes variation and selection as the underlying processes that produce change and thus concentrates the attention on the mechanisms that drive these processes in three levels of analysis (micro, meso and macro). Understanding behavior from this perspective may lead to the design of effective policies. In the following section the problem of urban mobility is framed under this perspective.. 5 Urban mobility as a product of an evolutionary system 5.1. The concept of urban mobility. Urban mobility refers to the mobilization of people within a city. In turn, a city may be understood as a social system (Marcus, 1985) where the interaction among people produces complex behaviors. Within an evolutionary framework, “the pattern generation of urban mobility can be explained in terms of organization processes in complex self adaptive systems” (Pulselli, Ratti, & Tiezzi, 2006). Hence, urban mobility under the framework of evolutionary ontology proposed by Dopfer will be understood as the product of an evolutionary system; an emergent behavior resulting from the creation, adoption and utilization of problem-solving rules carried out by rule-maker and rule using individuals. In the following section such system and the way how urban mobility is produced are described.. 18.

(22) 5.2. Description of the system. In the context of urban mobility, individuals solve the problem of going from one point (origin) to another (destination) within a city. The ways in which such origin-destination (OD) problems are solved, are defined by problem-solving rules. Each individual has a portfolio of options (rules) for solving the OD problem: to walk, to take a bus, to take a taxi, to drive a private vehicle, etc. Every time an individual needs to solve the OD problem, he selects a rule from the rule portfolio and uses it in his own particular way; an individual may select different OD problem-solving rules in time. The particularity of the way in which an agent uses a rule is determined by his personal characteristics, rationality and preferences; hence, many agents may use the rule of driving a private vehicle, but the speed of the vehicle (assuming free-traffic-flow), the driving style, the chosen route, among other elements will vary from agent to agent. Examples of this heterogeneity among individuals may be drawn from differences in income levels, geographical location, culture, education and occupation. In this problem, the unit of selection is rules; these are created and carried by agents who require solving the OD problem. The generic level of rules is characterized by the mode of transportation used (using a bus, driving a car etc.) whilst the operant level is evidenced by the particular way in which the agent uses the rule. Selective pressures such as communication among agents, new public policies and personal experience may trigger the origination of novel rules (e.g. use a new transport mode) and/or the adoption of existing rules. These rules (considered to be knowledge units) will evolve through variation and selection processes and the resulting rule dynamics will produce what is observed as urban mobility. To illustrate and explore the dynamics described above, an Agent-Based model was built. In the following sections such model is described and the rule dynamics is explored using simulation results from the model.. 5.3. Agent-Based Simulation model. 5.3.1 Why build an Agent-Based simulation model? Agent-based models, as suggested by the name, focus on individuals‟ actions and the dynamics resulting from their interaction. Furthermore, these models allow repeated experimentation under different scenarios before experimenting directly within reality (Pavón, Arroyo, Hassan, & Sansores, 2008) and facilitate the understanding of the system modeled through graphical display of behavior (Pavón, Arroyo, Hassan, & Sansores, 2008). In this way “this approach facilitates the study of how social phenomena emerge, that is, how the interactions and varied behaviors of individual agents produce structures and patterns” (Sansores, Pavón, & Gómez-Sanz, 2006), and as a result, has been chosen to portray and facilitate the understanding of urban mobility as product of an evolutionary system. 5.3.2 Description of the model The model was built in NetLogo using as a starting point a model that simulates traffic jams in a 2 lane road (Wilensky, 1998); the code is included in Annex 1. In this model, 19.

(23) ORIGIN. DESTINATIO N. agents may choose from an initial portfolio of two rules that allow solving the OD problem: taking a car and taking a bus. All agents need to go from a specific point (O) to a destination (D); given the illustrative purpose of the model, the origin and destination points are the same for all agents (See Figure 9.). HOW?. Figure 9 The origin-destination problem. Agents As mentioned above, agents in the model are heterogeneous. This heterogeneity is characterized in the model by three characteristics associated with agents: level of preference for velocity, level of preference for comfort, level of preference for cost, income level and level of frustration. The first three attributes indicate how much importance the individual gives to velocity, comfort and cost respectively. The level of income determines the capacity of an agent to afford transportation and the level of frustration indicates the inconformity of the individual towards the perceived situation based on his own experience. Preference levels for speed, comfort and cost are assigned randomly, income levels are normally distributed and its parameters can be determined by the modeler, and frustration is initialized at cero for all agents. Transport modes There are four possible transport modes: bus, private car, rickshaw and walking. Each mode has three attributes: speed, comfort level and cost. Comfort of modes is defined by the number of stops the mode makes while circulating, based on the premise that if the vehicle stops too many times the journey becomes uncomfortable. Cars and buses circulate on the street, and rickshaws and walkers circulate on the sidewalk. Each transport mode has different speed-limits and way of circulating; moreover, each vehicle, within a transport mode has its own particularities such as its speed, the stops it needs to make and the patience level required for it to change lanes. This particular mode of behavior associated with each vehicle evidences the operant level of rules. The creation of new rules The creation of new rules (new transport modes) is triggered when an agent accumulates enough frustration with regard to the situation; the motifs for frustration to increase are based on congestion levels (low average speeds or too many stops) and scarcity of transport supply. In the model, agents with sufficiently high levels of frustration may 20.

(24) innovate, though with a low probability, and create one of two rules: use a rickshaw or to walk. Both of these rules do not affect road traffic flow because they circulate on sidewalks. Rickshaws and walking only become available for selection once an agent has innovated and hence introduced them into the portfolio of OD-problem solving rules. Rule selection Rules are the unit of selection. Agents select rules based on their own trip experience, interaction with other agents, income level and environmental conditions. Experience, interaction and income levels serve as the criteria of selection whilst the environmental conditions (average speed, comfort and cost of each mode) serve as a reference for selection. The experience and interaction affect individuals‟ preferences for speed and comfort and this, in turn, determine which rule will be selected for the next trip; agents will select the mode that offers the best experience in terms of what they prefer most: speed or comfort. Experience Every time an agent reaches the destination point, he actualizes his level of preference for speed and comfort by evaluating his own experience based on the trip‟s average speed and comfort (measured in terms of the number of stops). Interaction among agents Furthermore, the individual‟s preferences are affected by the interaction with other agents; this sociality influences rule adoption according to a proximity ratio: if the majority of neighbors of a given agent prefer a particular rule over others then is more likely that the agent will try such a rule. Once an agent has selected the rule that fits best his preferences he searches for an available vehicle that serves his selection. Availability is determined by the number of people that can fit per vehicle (1 for private vehicles and rickshaws and 15 for buses); in the case of the walking mode, individuals do not need to check for availability. However, it is possible that no vehicles corresponding to the mode selected are available; in this case and depending on the transport mode, agents may introduce new vehicles (rickshaws and cars) or increase their frustration levels because they cannot mobilize. The introduction of new cars, however, also depends on the individual‟s income level which will determine whether or not he can afford another vehicle. Income level In general, the income level will determine whether an agent can access a transport mode or not depending on its cost. However, if this income-level is sufficiently high so that the agent can afford more than one transport mode, it will also act as a selective criterion. Policies that can be implemented In the model, the role of the planner is incorporated with the possibility of implementing policies. There are two types of policies that can be implemented: vehicle restriction and public transport related. Specifically, the policies that can be implemented are:. 21.

(25) Vehicle restriction policies: Internalizing private vehicle use costs: this policy may be implemented by increasing the cost of using a private vehicle for the journey. Direct restriction on vehicle circulation. 40% of the vehicles circulating are deleted from the system. Public transport related: Introducing or taking away buses from the system. Reducing the cost of taking a bus. Introducing an exclusive bus lane. Dynamics The use, creation and selection of rules produce traffic flow; however, congestion might start to build up as new agents enter the system because of population growth. Population growth can be controlled externally by modifying the growth percentage. Figure 10 shows a graphical representation of the dynamics of the system and Figure 11 shows the interface of the simulation model.. Figure 10 Graphical representation of the dynamics of the system.. 22.

(26) Figure 11 Simulation world for urban mobility. Seven indicators are used to evaluate mobility levels: street congestion (vehicles per road area), sidewalk congestion (vehicles and people per street area), percentage of agents with access (affordability) to use private vehicles, percentage of agents with access to buses. 5.3.3 Micro-meso-macro levels The simulation model shows how urban mobility is produced by the interaction among rule-user and rule-maker individuals. Following Dopfer‟s evolutionary ontology, further analysis of the dynamics observed may be drawn by identifying the micro, meso and macro levels within the system. In the micro level every agent has a portfolio of OD-problem solving rules (among other problem solving rules) to choose from. In the meso level, for every generic rule there is a rule population. Each population represents an instance of the meso level; hence, in the model one can find four meso instances, one for each transport mode. In the macro level, there is a coordinated structure of the different rule populations in the system. Figure 12 shows a graphical representation of the micro-meso-macro schema.. Figure 12 Micro-meso-macro schema in urban mobility. 23.

(27) In each of these levels, change is driven by variation and selection processes; successful selected variations are preserved through retention mechanisms. In order to observe these processes and the rule dynamics that emerges from them, an example of the behavior of the system is analyzed in the following section. 5.3.4 An initial simulation to understand rule dynamics In this section, an initial simulation is explained to understand rule dynamics within the micro-meso-macro schema. Given the illustrative purpose of this section, cost sensibility was not considered in this simulation; transport modes had no cost and hence population income levels did not affect variation, selection and retention of rules. Consequently, accessibility indicators are not considered either. With the simulation model we can explore how the system behaves when it is exposed to different selective pressures. Figure 13 shows simulation results for different rule populations (car, bus, rickshaw and walking) and the congestion levels on the street and the sidewalk. The graph is divided in four phases. Each phase represents important changes in the conditions of the environment (e.g. a new exclusive bus lane or demographic growth). These changes become selective pressures that influence the innovation or adoption of different rules. The figure highlights innovation events. This rule dynamics will be explained. 1. 3. 2. 4. Rule populations 200 180 160. Agents. 140. Bus Car Rickshaw Walking. 120 100. 80 60 40 20. 1 54 107 160 213 266 319 372 425 478 531 584 637 690 743 796 849 902 955 1008 1061 1114 1167 1220 1273 1326 1379 1432 1485 1538 1591 1644 1697 1750 1803 1856 1909 1962 2015 2068 2121 2174 2227 2280 2333 2386 2439 2492 2545 2598 2651 2704 2757 2810 2863 2916 2969 3022 3075 3128 3181 3234 3287 3340 3393. 0. Time (ticks). Selective pressures. Demographic growth. Innovation. Exclusive bus lane. Demographic growth. Sidewalk Street 1 69 137 205 273 341 409 477 545 613 681 749 817 885 953 1021 1089 1157 1225 1293 1361 1429 1497 1565 1633 1701 1769 1837 1905 1973 2041 2109 2177 2245 2313 2381 2449 2517 2585 2653 2721 2789 2857 2925 2993 3061 3129 3197 3265 3333. Congestion level (%). Congestion level 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0. Time (ticks). Figure 13 An initial simulation: Example of rule dynamics. 24.

(28) Phase 1. Initially, agents can only choose from a set of two rules: to use a car or to take a bus.. Phase 1 shows how car use rapidly outgrows bus taking and reaches stability. The system quickly responds to environmental conditions (low congestion levels) as agents select from their rule portfolio the mode that best solves the transportation problem in terms of ride speed and ease. Phase 2. More agents are introduced at the beginning of this phase (demographic increase),. the stability reached at the end of phase 1 is disturbed (macro-variation); however the system is still able to resist more agents that choose the rule of car use. Street congestion increases but still it is tolerated. It is important to note that agents are continuously selecting rules (micro level) and that this selection makes car-use rule population to grow (meso-selection). Nevertheless, the system quickly reaches stability again (macroselection). Knowledge self-organizes in spite of the demographic challenge; there is a robust behavior pattern of the populations of both rules. Phase 3. With the introduction of an exclusive bus lane and the increased congestion level. on the street from phase 2, rule populations are finally de-coordinated (macro-variation). Bus-taking rule population increases as a response to the favorable new environmental conditions (the average bus speed increases), car-use rule population decreases. The system adapts to the new conditions and redistributes rule frequencies accordingly. However, even though agents choose the best possible mode according to their own particular preferences (speed or ease) as a response to the congestion level on the street, some agents search for new and different ways of transportation. Indeed in some point of phase three one agent comes up with the idea of travelling in a rickshaw (micro-variation); a little bit later another agent generates another new rule (walking). Once the rule of rickshaw use is adopted by an agent, given its successful performance, several agents replicate the rule and the rickshaw population rule is created (meso-variation and mesoselection). In the same way, a rule-population can also disappear given a poor performance. An example is the bus-taking rule population: with the emergence of rickshaws and walkers, the use of bus becomes not attractive for agents and hence it is not selected in several points of the simulation; this leads to the radical reduction (and sometimes total elimination) of the bus rule population. The simulation shows successful and unsuccessful innovations as responses to unfavorable conditions in the environment wherein agents search for better solutions. If innovations are successful (as in the case of the rickshaw) they can be adopted by several agents. The knowledge system self-organizes from low-level decisions. Moreover, the preferences of each agent change through time according to social processes (imitation, proximity ratio) and individual experience. Knowledge is heterogeneous and dynamic. Phase 4. Further demographic growth is introduced. Again, there is dynamic self-. organization depending on the environmental conditions (e.g. congestion level on the street or on the sidewalk). In particular there is differential reproduction of the car-use rule which becomes stable and dominant in this system. Simulation results show how system‟s performance is produced from the interaction among agents that behave according to a set of simple rules applied to their own local conditions and preferences. 25.

(29) Throughout the simulation there is a continuous response of the system to selective pressures that leads to adaptive self-organization.. 5.4. Policy implementation: a general analysis. In this section, the effects of the implementation of vehicle restriction policies (direct restriction on circulation and internalizing vehicle use costs) and implementing an exclusive bus lane are explored by exposing the system to each of these using the simulation model. The initial conditions of the simulation are listed in table 3.. Initial population Initial bus supply Bus cost Car cost Rickshaw cost Walking cost Mean income level Mean standard deviation of income level % of population with car purchasing power. 127 5 2000 10000 1500 0 3.5 1.5 100%. Table 3 Initial simulation conditions. Figure 14, 15 and 16 show simulation results for different rule populations (car, bus, rickshaw and walking), the congestion levels on the street and the sidewalk, frustration levels that lead to innovation and the accessibility indicators under different selective pressures. Accessibility indicators evaluate agents‟ capacity to afford (in the case of private vehicles) or find available transport (in the case of buses). The graphs are divided in phases. Each phase represents important changes in the conditions of the environment, including demographic growth and implementation of different public policies. These changes become selective pressures that influence the innovation or adoption of different rules. In each of these experiments, initially agents can only choose from a set of two rules: using a car or taking a bus. The graphs are divided in two phases: before and after implementing the respective policy. In every case, demographic growth is introduced in the first phase. The graphs show how rule populations increase (graph A), as new agents enter the system increasing the street congestion and thereby increasing the frustration level (measured in terms of the frustration level of the agent with the maximum frustration). In this phase, accessibility remains fairly constant given that all agents can purchase a private vehicle and bus supply is increased every time more buses are required. Once the congestion level surpasses 45%, a policy is implemented. The second phase is explained in each case.. 26.

(30) A. Rule populations Phase 1: Demographic growth. Phase 2: Implementation of vehicle restriction policy. 300. Agents. 250 200. "Bus". 150. "Car". 100. "Rickshaw". 50. "Walking". x 12 25 38 51 64 77 90 103 116 129 142 155 168 181 194 207 220 233 246 259 272 285 298 311 324 337 350 363 376 389 402 415 428 441 454 467 480 493 506. 0. Time (ticks). B. Congestion Congestion level. 2.5. 2 1.5 1. "Sidewalk congestion" "Street congestion". 0.5. x 13 27 41 55 69 83 97 111 125 139 153 167 181 195 209 223 237 251 265 279 293 307 321 335 349 363 377 391 405 419 433 447 461 475 489 503. 0. Time (ticks). 300 250. 200 150. "max-frustration". 100 50. 0 x 13 27 41 55 69 83 97 111 125 139 153 167 181 195 209 223 237 251 265 279 293 307 321 335 349 363 377 391 405 419 433 447 461 475 489 503. Maximum frustration level. C. Maximum frustration level 350. Time (ticks). "Private vehicle access (%)" "Bus access (%)". x 14 29 44 59 74 89 104 119 134 149 164 179 194 209 224 239 254 269 284 299 314 329 344 359 374 389 404 419 434 449 464 479 494 509. %. D. Accessibility 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0. Time (ticks). Figure 14 Direct restriction on vehicle circulation. 27.

(31) x 11 23 35 47 59 71 83 95 107 119 131 143 155 167 179 191 203 215 227 239 251 263 275 287 299 311 323 335 347 359 371 383 395 407 419 431. %. x 10 21 32 43 54 65 76 87 98 109 120 131 142 153 164 175 186 197 208 219 230 241 252 263 274 285 296 307 318 329 340 351 362 373 384 395 406 417 428 439. Frustration level. x 11 23 35 47 59 71 83 95 107 119 131 143 155 167 179 191 203 215 227 239 251 263 275 287 299 311 323 335 347 359 371 383 395 407 419 431. Congestion level. x 10 21 32 43 54 65 76 87 98 109 120 131 142 153 164 175 186 197 208 219 230 241 252 263 274 285 296 307 318 329 340 351 362 373 384 395 406 417 428 439. Agents. Phase 1: Demographic growth. A. Rule populations. 300. Phase 2: Internalizing vehicle use costs. 250. 200. 150. "Bus". 100. "Car". 50. "Rickshaw". 0 "Walking". Time (ticks). 2.5. B. Congestion. 1.5. 2. 1. 0.5 "Sidewalk congestion". "Street congestion". 0. Time (ticks). 400. C. Maximum frustration level. 350. 300 250. 200. 150 100 "max-frustration". 50. 0. Time (ticks). 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0. D. Accessibility. "Private vehicle access (%)". "Bus access (%)". Time (ticks). Figure 15 Internalizing vehicle use costs. 28.

(32) x 28 57 86 115 144 173 202 231 260 289 318 347 376 405 434 463 492 521 550 579 608 637 666 695 724 753 782 811 840 869 898 927 956 985 1014 1043 1072 1101. % x 26 53 80 107 134 161 188 215 242 269 296 323 350 377 404 431 458 485 512 539 566 593 620 647 674 701 728 755 782 809 836 863 890 917 944 971 998 1025 1052 1079 1106. Frustration level. x 28 57 86 115 144 173 202 231 260 289 318 347 376 405 434 463 492 521 550 579 608 637 666 695 724 753 782 811 840 869 898 927 956 985 1014 1043 1072 1101. %. x 26 53 80 107 134 161 188 215 242 269 296 323 350 377 404 431 458 485 512 539 566 593 620 647 674 701 728 755 782 809 836 863 890 917 944 971 998 1025 1052 1079 1106. Agents. Phase 1: Demographic growth. A. Rule populations. 250. Phase 2: Implementation of exclussive bus lane. 200. 150. 100 "Bus". 50 "Car". "Rickshaw". 0 "Walking". Time (ticks). 0.7. B. Congestion. 0.6. 0.5. 0.4. 0.3. 0.2 "Sidewalk congestion". 0.1. 0 "Street congestion". Time (ticks). 80 70 60 50 40 30 20 10 0. C. Maximum frustration. "max-frustration". Time (ticks). 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0. D. Accessibility. "Private vehicle access (%)". "Bus access (%)". Time (ticks). Figure 16 Implementation of exclusive bus lane policy. 29.