César Ducruet
email: ducruet@parisgeo.cnrs.fr

Igor Lugo
email: lugoigor@gmail.com

STRUCTURE AND DYNAMICS OF TRANSPORTATION NETWORKS: MODELS, METHODS, AND APPLICATIONS *

methods in the late 1990s. The research field of complex networks, mostly led by physicists eager to test their models and measures on real-world cases, provided many analyses of the structure and dynamics of large-scale networks of all kinds. Among them, transport networks have received much attention, thereby providing new ways of understanding their internal efficiencies and vulnerabilities.

 and comes as a complement to other approaches such as circulation routing and flow optimization which strongly focus on transport costs. So far, there remains little overlap and interaction between the different approaches. This state of affairs as well as the fast evolution of the field motivate this chapter, which proposes a review of existing studies of transport networks from a methodological point of view.

We propose distinguishing amongst the structure and the dynamics of transportation networks. The first section discusses the static dimension (structure) and reviews how transportation networks have been defined and analyzed with regard to their topology, geometry, morphology, and spatial structure. It presents a critical overview of main global (network level) and local (node level) measures and examines their usefulness for understanding transportation networks. The second section explores the dynamics of transportation networks, their evolution, and the properties underlying such evolutions. Each section provides a brief background of the relevant literature, concrete applications, and policy implications in various transport modes and industries, with an interdisciplinary focus. A discussion is provided evaluating the legacy of reviewed works and potential for further developments in transport studies in general.

Structure of Transportation Networks

Transport networks are mostly studied in a static fashion. Research in this field is very voluminous and diverse, but one central goal is to highlight the overall characteristics of the networks structure based on its topology, geometry, morphology, and traffic flows. We divide the literature review between the global level, which is interested in describing the entire network, and the sub-levels, which look either at groups or individual nodes within the network.

Introduction

The study of transport networks has long been at a crossroads between various scientific disciplines. Traditionally, transport networks are studied from a graph theory perspective, which is a branch of mathematics proposing concepts and measures about the topology of networks considered as sets of nodes (vertices) connected by links (edges). Limited computational power and the scarcity of relevant datasets limited the analysis to relatively small networks, most of which are planar networks (i.e. with no crossings between edges) and accessibility problems based on pure topology. Decreasing interest for structural approaches in social sciences partly caused the stagnation of transport network analysis since the 1970s, notwithstanding progress brought in by Geographical Information Systems (GIS) and and spatial economics from the 1980s onwards [START_REF] Waters | Spatial Dynamics, Networks and Modelling, chapter Network and Nodal Indices. Measures of Complexity and Redundancy: A Review[END_REF]. Technology and information improvements as well as the wide popularization of the "network" concept contributed to the emergence of new analysis

Global Level

Transport networks belong to a wider category of spatial networks, because their design and evolution are physically constrained (e.g. Euclidian distance) as opposed to non-spatial networks such as the Internet, scientific collaborations, multinational firms, social networks, and biological systems such as neuronal networks [START_REF] Boccaletti | Complex networks: structure and dynamics[END_REF][START_REF] Gastner | The spatial structure of networks[END_REF][START_REF] Blumenfeld-Lieberthal | The topology of transportation networks: a comparison between different economies[END_REF]. The physical grounding varies in relevance depending on the transport mode considered. Urban streets, roads, and railways are composed of track infrastructure, while maritime and air transports remain vaguely defined due to their higher spatial flexibility except for the location of terminals themselves [START_REF] Rodrigue | Geography of Transport Systems[END_REF]. Maritime networks remain more constrained than airline networks due to coastlines [START_REF] Xu | Empirical analysis of the ship-transport network of china[END_REF]. River networks typically form basins and can be classified as trees or dendrograms [START_REF] Banavar | Size and form in efficient transportation networks[END_REF].

Most research on transport networks has focused on planar networks (e.g. roads, railways) due to easier access to infrastructure data allowing topological measurements and because the planning and operation of such networks concern everyday mobility at the urban (e.g. street, bus, subway) or regional level (e.g. highway, interstate lanes). Other transport networks without track infrastructure (e.g. air, sea) necessitate the use of traffic data to build a graph of ephemeral links from either vehicle movements or scheduled service. Partly due to the absence of comparable data for urban studies, airline networks have become central in the analysis of systems of cities on various levels. Maritime transport has recently received more interest due to the use of newly available data on carrier services and vessel movements [START_REF] Ducruet | Centrality and vulnerability in liner shipping networks: revisiting the northeast asian port hierarchy[END_REF].

In their review of the measures synthesizing the structure of communication networks, [START_REF] Béguin | The shape of the transportation network and the optimal location of facilities. how to measure the shape of a network[END_REF] introduce four different schools: graph theory, matrices of the shortest paths, geomorphometry (quantitative land surface analysis), and eigenvalues. Matrices of the shortest paths refer to the search for specific configurations in the network, such as the minimum cost tree, which is the part of the network in which all nodes remain connected by the lowest cost path. This approach can be at the global level to extract the optimal route from the network or at the local level to zoom to specific nodes using algorithms such as Kruskal, Floyd, or Dijkstra, among others [START_REF] Ducruet | Graph simplification and partitioning[END_REF]. Geomorphometry mainly examines river networks based on several indicators such as the number of bifurcations and the length of the river, its number of junctions. There are two types of eigenvalues approaches: the application of multivariate statistical methods to adjacency matrices and the use of blockmodeling and structural equivalence to determine groups of nodes sharing similar connectivity patterns in the network [START_REF] Beauguitte | Blockmodeling and equivalence[END_REF]. Based on the simplification of real-world places and pathways into graphs, i.e. groups of nodes (vertices) connected by some links (edges), a wide set of concepts and measures have been proposed by graph theory since the seminal Seven Bridges of Koenigsberg problem proposed by Leonhard Euler in 1735. The main questions addressed are whether different transport networks share comparable properties and according to which criteria.

The integration of graph theoretical methods in geography and regional science with related applications to transport networks dates back to the works of [START_REF] Garrison | Connectivity of the interstate highway system[END_REF], [START_REF] Kansky | The structure of transportation networks: Relationships between network geography and regional characteristics[END_REF], Haggett and[START_REF] Haggett | Network Analysis in Geography[END_REF][START_REF] Garrison | Connectivity of the interstate highway system[END_REF][START_REF] Garrison | Transportation Geography: Comments and Readings, chapter Graph theoretic concepts[END_REF]. The first set of studies was characterized by limited data, low computational power, and few modeling techniques [START_REF] Xie | Measuring the structure of road networks[END_REF] but is still used to analyze the structure of transport networks. Most common measures include the number of nodes and links (i.e. network size), the length and diameter of the network, its connexity (continuity of the network in terms of the number of connected components), connectivity (more or less optimal distribution of links among the nodes), and nodality. Notably, the three main indices Alpha, Beta, and Gamma proposed by [START_REF] Kansky | The structure of transportation networks: Relationships between network geography and regional characteristics[END_REF] do not consider the real length, quality, and weight of the links;1 networks of equal size may exhibit contrasted topological forms [START_REF] Béguin | The shape of the transportation network and the optimal location of facilities. how to measure the shape of a network[END_REF][START_REF] Kurant | Extraction and analysis of traffic and topologies of transportation networks[END_REF]. However, they remain useful for describing the changing structure of one given network [START_REF] Scott | Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks[END_REF][START_REF] Xie | Modeling the growth of transportation networks: A comprehensive review[END_REF] [START_REF] Barabási | Emergence of scaling in random networks[END_REF] define scale-free networks using a high exponent (over 1 and/or between 2 and 3) of the power-law slope drawn based on plotting the frequency of nodes versus the degree centrality distribution (i.e. the number of edges connecting direct neighbors). Many nodes have a poor number of connections, while only a few nodes multiply them, thus making the network highly heterogeneous or disassortative (Figure 1). In comparison, small-world networks [START_REF] Watts | Collective dynamics of small-world networks[END_REF] are defined by a high average clustering coefficient and a short diameter, which illustrates a high density of links and the existence of many cliques based on the probability that two randomly chosen neighbors of a node are also direct neighbors of each other. The average shortest path length is a measure of network efficiency [START_REF] Barabási | Statistical mechanics of complex networks[END_REF].

Considering actual account traffic, the rich-club index measures to what extent large degree nodes are strongly connected to each other. Scale-free and small-world networks tend to concentrate at a few high-order connections, whereas road networks are characterized by a large diameter (i.e. maximum path length).

Other indices have been proposed, such as the assortativity coefficient, which highlights to what extent nodes of comparable connectivity are connected to each other in the network. Such properties imply dynamics and evolutionary paths that are specific to transport networks. [START_REF] Hu | Empirical analysis of the worldwide maritime transportation network[END_REF] and the global air transport network [START_REF] Guimera | The worldwide air transportation network: anomalous centrality, community structure, and cities global roles[END_REF] confirm their overall scale-free properties with regard to the existing literature on hub-and-spoke strategies of carriers (see [START_REF] Fleming | Spatial characteristics of transportation hubs: centrality and intermediacy[END_REF][START_REF] O'kelly | A geographer's analysis of hub-and-spoke networks[END_REF]). While a relatively high vulnerability of the network lays upon few large nodes, the truncated distribution reveals the costs of creating new links at large nodes facing congestion problems, as seen in other case studies of air transport networks [START_REF] Li | Statistical analysis of airport network of china[END_REF][START_REF] Guida | Topology of the italian airport network: A scale-free small-world network with a fractal structure[END_REF], public transportation networks [START_REF] Latora | Is the boston subway a small-world network?[END_REF][START_REF] Sienkiewicz | Statistical analysis of 22 public transport networks in poland[END_REF][START_REF] Von Ferber | Scaling in public transport networks[END_REF][START_REF] Xu | Empirical analysis of the ship-transport network of china[END_REF] and railway networks [START_REF] Sen | Small-world properties of the indian railway network[END_REF].

Since most research focuses on one single network, there is a growing awareness that transport networks may be interdependent through issues of interconnectivity and the vulnerability of tightly coupled infrastructures regarding cas-cading failures, breakdowns, and attacks [START_REF] Van Geenhuizen | Interconnectivity of transport networks: a conceptual and empirical exploration[END_REF][START_REF] Vespignani | Complex networks: The fragility of interdependency[END_REF][START_REF] Buldyrev | Catastrophic cascade of failures in interdependent networks[END_REF]. Notably, [START_REF] Zhang | Dynamic game theoretic model of multilayer infrastructure networks[END_REF] This is also apparent in the work of [START_REF] Parshani | Inter-similarity between coupled networks[END_REF] on the inter-similarity between coupled maritime and air transport networks, which shows that wellconnected airports tend to couple with well-connected seaports in general, based on their respective geographic locations and on their topological attributes. Finally, [START_REF] Jin | China's regional transport dominance: Density, proximity, and accessibility[END_REF] propose an indicator of transport dominance applied simultaneously to freeway, railway and airline networks in China, stressing the importance of large urban concentrations and the role of distant cities serving as hubs for Western areas.

Local Level

Various local measures of networks have also been developed [START_REF] Taaffe | Geography of Transportation[END_REF][START_REF] Cliff | Applications of Graph Theory, chapter Graph Theory and Geography[END_REF][START_REF] Dupuy | Cities and highway networks in europe[END_REF][START_REF] West | Introduction to Graph Theory[END_REF][START_REF] Degenne | Introducing Social Networks[END_REF]. The goal is to compare the relative position of nodes in the network and to highlight groups of nodes in the network. While a full list of measures would run beyond the scope of this chapter, it is possible to organize them by those indicating the situation of the node in the entire network and those focusing on the relations of the node with its adjacent neighbors.

The most common global measures of a given node include:

• betweenness centrality: number of possible positions on shortest paths;

• eccentricity (or associated number, Koenig number): number of links needed to reach the most distant node in the graph;

• Shimbel index (or Shimbel distance, nodal accessibility, nodality): sum of the length of all shortest paths connecting all other nodes in the graph.

The inverse measure is also called closeness centrality or distance centrality.

Other local measures are those looking at the neighborhood of a given node:

• degree (or degree centrality): number of adjacent neighbors. The weighted degree is the sum of weights on adjacent links;

• hub dependence: share of the strongest traffic link in total traffic, a measure of vulnerability;

• average nearest neighbors degree: indicates to what extent the node is surrounded by large or small nodes;

• clustering coefficient: proportion of observed closed triplets in the sum of all possible closed triplets, a measure of tightness and density.

Plotting together some of the aforementioned measures may highlight specific features of transportation networks. For instance, nodes with low degree centrality and high betweenness centrality reveal their role as bridges between different subgroups, i.e. as a strategic position or intermediacy. Some hubs may also act as a redistribution platform within their adjacent regions, thus multiplying their links in addition to their bridge role (e.g. feeder links in liner shipping, air or trucking). In maritime networks, [START_REF] Deng | Worldwide marine transportation network: efficiency and container throughput[END_REF] Local measures may or may not take into account the valuation of the edges concerned (i.e. weighted or non-weighted). This is of high relevance for transport studies, since transport networks are better understood by the usage level (e.g. number of passengers, tons, vehicles, capacity) than by their sole topology based on a binary state (i.e. presence or absence of links). The weighted degree centrality usually corresponds to the sum of edges traffic, sometimes called node strength. The hub dependence index, which corresponds to the percentage of the strongest traffic edge in the total traffic of each node, is a measure of vulnerability, notably highlighting the contrast for Shenzhen port between its traffic growth and its maintained dependence upon the Hong Kong hub [START_REF] Ducruet | Centrality and vulnerability in liner shipping networks: revisiting the northeast asian port hierarchy[END_REF]. In their study of the worldwide air transport network, [START_REF] Barrat | The architecture of complex weighted networks[END_REF] demonstrated that the formation of cliques among major airports (also called rich-club phenomenon) is readable only using traffic weights. The inclusion of edge weight in the analysis can indeed reveal hidden patterns beyond the sole topology, such as the small-world structure of commuter flows in Italy [START_REF] Demontis | Modeling commuting systems through a complex network analysis: A study of the italian islands of sardinia and sicily[END_REF] and the identification of different city types through the relative efficiency of their urban street patterns and length compared to optimal configurations of greedy triangulation and minimum spanning tree [START_REF] Cardillo | Structural properties for planar graphs of urban street patterns[END_REF].

Motivated by practical and policy implications, transport networks analysis has also benefited from further developments in accessibility studies. Various algorithms, such as the Koenig number and the Shimbel index, have been used

to describe nodal accessibility in numerous empirical studies. The Shimbel index was used in several case studies to reveal nodal accessibility for European regions in the context of integration [START_REF] Gutierrez | Accessibility in the european union: the impact of the trans-european road network[END_REF] and Belgian crossroads [START_REF] Lannoy | The accessibility of nodes in the belgian road network[END_REF]; the impact of deregulation and hub formation on air accessibility in China [START_REF] Choua | Airline deregulation and nodal accessibility[END_REF][START_REF] Shaw | Hub structures of major us passenger airlines[END_REF] and in Southeast Asia [START_REF] Bowen | Airline hubs in southeast asia: national economic development and nodal accessibility[END_REF][START_REF] Bowen | Network change, deregulation, and access in the global airline industry[END_REF]; and the impact of railway development on urban accessibility in Japan [START_REF] Murayama | The impact of railways on accessibility in the japanese urban system[END_REF]. Examining the Indianapolis city road network, [START_REF] Gleyze | Making allowances for spatial and network effects when assessing indicators on infrastructure network nodes[END_REF] refines topological measures of centrality and eccentricity by distinguishing between network and spatial effects. [START_REF] Chapelon | Accessibility as a marker of disparities of influence among port cities in europe[END_REF] applies the widely used Floyd algorithm of the shortest path, taking into account topological features and circulation constraints such as national regulations (e.g. speed limits), highway tolls, the types and capacities of main roads, and the travel time including loading /unloading delays at multimodal junctions (e.g. ferry crossings). Based on such criteria, [START_REF] Chapelon | Accessibility as a marker of disparities of influence among port cities in europe[END_REF] calculated the topological accessibility of ports to population and regional wealth potentials within a 6, 36, and 72 hour drive (Figure 2). The results clearly the privileged situation of North European Range ports regarding hinterland coverage and connectivity, while the correlation between road accessibility level and port throughput volume remains only moderately significant.

The search for subgroups of nodes or clusters in a network is another central issue in network analysis with related applications on transport networks.

Sociologists "were the first to formalize the idea of communities, to devise mathematical measures of the number and cohesion of communities, and to develop methods to identify the subgroups of individuals within the network" [START_REF] Boccaletti | Complex networks: structure and dynamics[END_REF]. Such methods also allow for simplifying large datasets in order to make their structure more readable. In their general works on graph theory, [START_REF] Berge | [END_REF] and [START_REF] Bollobas | Modern Graph Theory[END_REF] define subgraphs (or subgroups) as the union of a subset of nodes and the edges linking them. All methods seek to find relevant measures in order to bisect a given graph based on closeness within resulting groups (see [START_REF] Wasserman | Social Network Analysis: Methods and Applications[END_REF][START_REF] Moody | Social cohesion and embeddedness: A hierarchical conception of social groups[END_REF]). Resulting subgroups exhibit various characteristics such as adhesion, with a centralization upon a leader node, cohesion, with more harmonized ties among nodes, cliquishness, exhaustiveness, reachability, and density [START_REF] Gleyze | Multilevel Analysis and Visualization of Geographical Networks[END_REF]. The identification of cut-sets allows the identification of the edges to be removed between subgroups, focusing on connections existence, quality and robustness within and between subgroups, with reference to the concept of clique.

The notion of modularity refers to the optimization of intra-group cohesiveness (measured by distance, structural similarity or dissimilarity among nodes) with regard to inter-group linkages.

One of the first methods introduced for delimitating coherent sub-regions was the Nystuen-Dacey or dominant flow algorithm [START_REF] Nystuen | A graph theory interpretation of nodal regions[END_REF], first applied to telecommunication flows among Oregon cities. Its numerous applications on weighted transport networks have revealed barrier effects in air and rail transport flows [START_REF] Cattan | Barrier effects: the case of air and rail flows[END_REF], which cities dominate the hierarchy of air flows [START_REF] Grubesic | Global airline networks and nodal regions[END_REF], and which ports act as pivots in maritime systems [START_REF] Ducruet | The worldwide maritime network of container shipping: spatial structure and regional dynamics[END_REF]. Retaining only the maximum flow link between each node simplifies the networks architecture and highlights the existence of so-called nodal regions. Independent nodes are those in which dominant flow connects a smaller node in terms of individual traffic size; subordinate nodes always connect a larger sized node. Notably, [START_REF] Cattan | Le monde au prisme des réseaux aériens[END_REF] depicts the lowering integration level of the worldwide airline system, observing its growing polarization by fewer large hubs and the disappearance of sub-regional systems formerly well connected internally. Figure 3 Because the Nystuen-Dacey algorithm neglects a large number of secondary links, other methods of graph clustering (or partitioning) have been preferred in order to take into account the complexity of the network [START_REF] Gleyze | Multilevel Analysis and Visualization of Geographical Networks[END_REF] and its multi-level organization. For instance, the application of strength clustering on commuter flows in France reveals the polycentrism, cohesion and fragmentation of urban areas [START_REF] Tissandier | Multilevel Analysis and Visualization of Geographical Networks, chapter Defining Polycentric Urban Areas through Commuting Cohesion[END_REF]. It has also been applied to air passenger flows among cities worldwide [START_REF] Amiel | Multi-level networks: The example of worldwide air passenger flows[END_REF], showing the importance of geographic proximity in the formation of clusters and the multi-level relations among them. For instance, Asian cities tend to form dense neighborhoods, while some global hubs do not belong to a specific cluster due to their intermediate role between regions. Other measures include the z-score (share of the cluster in a nodes connections) and the participation coefficient (connections to other clusters) applied to worldwide air traffic [START_REF] Guimera | The worldwide air transportation network: anomalous centrality, community structure, and cities global roles[END_REF]. The availability of air traffic data on city pairs has helped foster a vast number of studies on the centrality of cities in such networks (see [START_REF] Shin | World cities in asia: cliques,centrality and connectedness[END_REF]).

The bisecting K-means algorithm applied on all weighted maritime links among Atlantic ports verifies a strong interdependency within the Le Havre-Hamburg or North European range as well as strong ties among Iberian Peninsula ports and Brazilian ports. Other port groups are better defined by internal geographic proximity and peripherality from the whole system [START_REF] Ducruet | Centrality and vulnerability in liner shipping networks: revisiting the northeast asian port hierarchy[END_REF]. Overall, the only problem related to clustering methods is that there is no guarantee to reach the best result as it depends on the edge weight selected (e.g. traffic), and there is no indication about the right number of iterations needed unless it verifies valid hypotheses about the number, quality, and size of expected groups.2 Simpler methods have been proposed, such as deleting edges with high betweenness centrality [START_REF] Newman | Finding and evaluating community structures in networks[END_REF] or removing the highest or lowest degree centrality nodes [START_REF] Zaidi | Analysis, Structure and Organization of Complex Networks[END_REF]. Other approaches include clustering junctions on road networks [START_REF] Mackechnie | Detection and simplification of road junctions in automated map generalization[END_REF][START_REF] Gleyze | Making allowances for spatial and network effects when assessing indicators on infrastructure network nodes[END_REF]; the use of structural equivalence, blockmodeling, and lambda-set methods to study the regionalization of the US Internet backbone infrastructure network [START_REF] Gorman | Spatial small worlds: new geographic patterns for an information economy[END_REF]; and measures of node vulnerability in the Swedish road network [START_REF] Jenelius | Network structure and travel patterns: explaining the geographical disparities of road network vulnerability[END_REF].

Dynamics in Transportation Networks

Understanding transport development requires analyzing complex and dynamic processes that produce temporal changes in the transport system. Two main issues are addressed in the literature: how the spatial structure of transportation networks evolves over time and the mechanisms modifying this structure.

Several techniques have been developed to describe, compute, and simulate large-scale features of transport networks. Geographers describe topological transformations in the network based on intuitive mechanisms to replicate network geometries [START_REF] Kansky | The structure of transportation networks: Relationships between network geography and regional characteristics[END_REF][START_REF] Taaffe | Transportation expansion in underdeveloped countries: A comparative analysis[END_REF][START_REF] Garrison | The structure of transportation networks[END_REF][START_REF] Morrill | Migration and the growth of urban settlement[END_REF]; economists and urban planners compute and design statistical models for choosing the best design of the network from an explicit set of available variables and alternatives [START_REF] Schweitzer | Optimization of road networks using evolutionary strategies[END_REF][START_REF] Leblanc | An algorithm for the discrete network design problem[END_REF][START_REF] Yang | Models and algorithms for road network design: A review and some new developments[END_REF][START_REF] Gastner | The spatial structure of networks[END_REF][START_REF] Barthélemy | Optimal traffic networks[END_REF]; and engineers and physicists simulate emergent attributes of networks applying the concept of self-organization and agent-based models (ABM) (Barabási, 2002; Newman, 2003; Lam and Pochy, 1993; Helbing et al., 1997; Yamins et al., 2003; Yerra and Levinson, 2005; Fricker et al., 2009; Levinson and Yerra, 2006; Xie and Levinson, 2009). 3 In this section, we review briefly the application of Agent-Based Models (ABMs) to analyze the dynamics in transport networks. Although such an application is relatively new in the field of transportation networks, it provides important advantages over analytical models. For example, ABMs describe individual interrelationships and form emergent properties in the system. Therefore, ABMs are a class of computational models for simulating systems of autonomous components, called agents, and the relationships between them and their environment. Based on a set of simple behavioral rules, agents interact and produce unexpected collective behavior [START_REF] Newman | The structure and function of complex networks[END_REF]. From the perspective of complex systems, the self-organization process shows how global patterns or structures in a system appear without the influence of a central authority or planning entity [START_REF] Vicsek | Complexity: The bigger picture[END_REF]. Therefore, ABMs are a good option to study the dynamics in transportation networks, because they are evolutive models and allow for flexible application to diverse theoretical approaches.

For the purpose of analysis, we present two different methods to conduct an ABM application: generative and degenerative processes. Then, we point out some important factors that analyze the change and growth in the spatial configuration of the network. Finally, we suggest some significant features to be considered when designing ABMs for transport networks.

Generative vs Degenerative Methods

When the goal of the analysis is to design, create, and compare a simulation model to empirical evidence in transportation networks, there are two general procedures to analyze dynamics. The first is a generative method of network formation, which explains how nodes connect by edges over time and which situations are considered rest-points in this process. Starting from an unconnected graph, the process begins to connect each node based on different mechanisms related to edges, for example, the cost of building or maintaining the link. Another process to connect nodes is based on their hierarchical attributes. For example, a link is created between two nodes based on their degree of connectivity. This process is known as "preferential attachment" and explains the dynamics of networks that generate a power law degree distribution in a system [START_REF] Newman | The structure and function of complex networks[END_REF]. Applying such a process to transportation systems may reveal some emergent behaviors in planar and non-planar networks, such as hub-and-spoke configurations (e.g. airlines and shipping lines) and the limitations imposed by the spatial structure of transport networks (e.g. number of connections per node for road and railway paths). Therefore, preferential attachment not only explains the dynamics of a system based on nodes connectivity but also highlights the consideration of hierarchical attributes of nodes in order to explore the evolution of networks. The generative method has been used in various scientific fields, for example, in economics [START_REF] Goyal | Network of collaboration in oligopoly[END_REF][START_REF] Yamins | Growing urban roads[END_REF][START_REF] Kirman | Complex Economics, Individual and Collective Rationality[END_REF], geography [START_REF] Jiang | Geospatial Analysis and Modelling of Urban Structure and Dynamics[END_REF], and urban planning [START_REF] Wilson | Schedule-Based Modeling of Transportation Networks[END_REF].

On the other hand, the degenerative method proposes starting the analysis from a complete network, where all nodes are connected to each other. All edges represent possible paths that can be used in the system, but only the more valuable ones reinforce its existence and modify its attributes. For example, some local roads can reinforce their importance based on the number of users and the cost of maintenance as well as the hierarchy of nodes to which they are connected. Therefore, edges that are less valuable decrease in importance and in their probability to be considered part of a representative path. Civil engineering is the most common field of application for such a method (see [START_REF] Yerra | The emergence of hierarchy in transportation networks[END_REF], [START_REF] Leblanc | An algorithm for the discrete network design problem[END_REF], and [START_REF] Xie | Modeling the growth of transportation networks: A comprehensive review[END_REF]).

Both methods have the same purpose: explaining an endogenous change and growth that produces a particular network structure. For example, Fig-

ure 4 shows a planar network road system consisting of 150 nodes that are uniformly and randomly distributed in an array of 400x400 cells, where the initial configuration of the system in the generative method is shown in 4a, and the degenerative method is displayed in 4c. Phase 4b presents one possible outcome in the final structure of the network; the process behind this structure is a simple distance mechanism that connects shorter edges or deletes large edges to nodes.

Even though both methods start from different initial conditions and apply inverse distance mechanisms, they converge to explain the final configuration of the network. In other words, there are different stories to tell about a process that creates particular network configurations. With this in mind, pre-knowledge of the network structure, such as global/local measures, and its contextualization, such as social and economic conditions, make it possible to define a mechanism that well explains the dynamics behind such a configuration. Then, the dynamics can be related to local mechanisms particularly market areas, traffic flows, and socio-cultural characteristics [START_REF] Christaller | Die Zentralen Orte in Suddeutschland[END_REF].

Therefore, the next step in the process to make a simulation model is selecting those factors that guide the explanation of the evolution in the transportation network and specify them as parameters in the model.

Mechanisms for Changing and Growing

The evolution of the transportation network is closely related to the change and growth of central places. For example, [START_REF] Christaller | Die Zentralen Orte in Suddeutschland[END_REF] explains the existence, characteristics, and evolution of such places. Transportation networks are one of the principal factors to describe the number, size, and patter of spatial distribution of these places. The co-evolution process of central places and transport networks points out the strong connection between the dynamics of urban and transport systems, as seen in the works of [START_REF] Batty | Cities and Complexity. Understanding cities with cellular automata, agent-based models, and fractals[END_REF][START_REF] Batty | The size, scale, and shape of cities[END_REF] and [START_REF] Blumenfeld-Lieberthal | Geospatial Analysis and Modelling of Urban Structure and Dynamics, chapter Network Cities: A Complexity-Network Approach to Urban Dynamics and Development[END_REF].

The market factor applied to transport networks explains the importance of the transportation cost between places in order to maximize the trade-off between demand and supply of goods and services. The creation and maintenance of transportation systems depend on the distance between central places;

the higher the distance between places, the more expensive it is to sustain the transportation system. In other words, short links in transportation are preferable. Therefore, the market area affects the transportation cost and shapes some emergent patters in the structure of the network [START_REF] Fujita | The Spatial Economy: Cities, Regions, and International Trade[END_REF].

In addition to the market factor, traffic is another important element that affects the structure of transportation systems. This explains the importance of the flow of individuals, goods, and services between central places. Components that describe this factor include the total travel time and speed on links [START_REF] Xie | Modeling the growth of transportation networks: A comprehensive review[END_REF]. Large-scale properties of the network, such as the existence and hierarchy of links in the system, are analyzed by the traffic factor [START_REF] De Dios | Modeling Transport[END_REF][START_REF] Leblanc | An algorithm for the discrete network design problem[END_REF]. The socio-cultural factor is a hierarchical order between central places showing the level of embedded systems across a particular geographical area, for example, an increased number of activities coordinated inside a city makes it more important compared to other cities. Economic and demographic variables, such as the Gross Domestic Product (GDP) and the urban population, valuate and describe the rank of a place in a system. Consequently, transportation links receive the effects of this factor, because more activities need to expand and distribute their influence to other places [START_REF] Krugman | The Self-Organizing Economy[END_REF][START_REF] Pumain | Fractals in urban geography: A theoretical outline and an empirical example[END_REF][START_REF] Batty | Cities and Complexity. Understanding cities with cellular automata, agent-based models, and fractals[END_REF].

As a result, depending on the spatial structure of the network, dynamics can be studied through the above factors and translated into parameters. Continuing with our example of a random planar network, links are classified into different levels according to the transportation cost. Each link has a specific cost associated to the distance between a node pair; the closer the nodes are, the easier it is to build and maintain their connection, and the less likely longer pairs are to exist. Defining a parameter beta, which provides a threshold value for selecting shorter links (distance of one link divided by the larger link), and modifying its value provides the results shown in Figure 5.

In the figure, 5a shows a topological structure when beta is defined as 0.15, meaning that links of bigger size (i.e. more than 15%), are not used to connect

Applications

Almost any transportation network shows properties of complex systems, and its structures can be explained by the self-organization process. Hence, ABMs are a natural method to search explanations of the evolution of such systems, because they provide flexible and novel applications. For instance, [START_REF] Martens | Geospatial Analysis and Modelling of Urban Structure and Dynamics, chapter The Dilemma of On-Street Parking Policy: Exploring Cruising for Parking Using an Agent-Based Model[END_REF] simulate parking behaviors in a city, and Blumenfeld-Lieberthal and Portugali ( 2010) develop an urban simulation model to study the dynamics of a system of cities. After specifying the method and the relevant parameters of the analysis, the next step is to design and implement simulation experiments.

At a glance, ABMs are computational methods conforming to a collection of autonomous agents, each of which has specific attributes and behavioral rules;

their interrelationships produce large-scale structures in the system. In order to apply ABMs to transportation networks, it is necessary to define the scale of the analysis and the type of agents in the system. The scale can be related to a region, a metropolitan area, or a city; the type of agents can be specified by nodes such as cities or terminals; and edges represent transportation links among them. The key point is to define an autonomous agent who has particular attributes and specific methods to interact with other agents and its surroundings. Attributes are intrinsic characteristics of the agent, such as its name, age, or location, and methods are mechanisms or behavioral rules that make possible the interaction with others and modify its attributes, for example, a method that changes the agent's location.

Applying such methods to the Mexican road system specifies two types of autonomous agents: cities as nodes and roads as edges. The former is defined as a central place with the attributes of location and population. The latter is defined as transportation links with the attributes of location and distance.

The first method compares the distance between links and returns a group of the shortest ones; the second method compares the distance between links and the number of habitants between nodes in order to return short links to connect more populated cities. In Figure 6, 6a displays the road system of Mexico, 6b

shows the simulation result based on short roads, and 6c exhibits a simulation output regard to short roads and large cities.

Although this example is very simple, note that the process behind emergent patterns is an auto-organization between agents without a central authority or planner that can be applied to the analysis of the existence, geometry, or hierarchy in road networks.

In addition to the rigorous application of ABMs to transportation networks, the results must be communicated in a straightforward, structured, and scientific way. Simulation models are usually validated through comparison to empirical data. The simulation also needs to achieve other requirements, such as analyzing temporal changes in the topology of the network based on different network paths, measuring structural properties of the network, and making a sensitivity analysis where emergent patterns in the network are related to dissimilar initial conditions [START_REF] Xie | Modeling the growth of transportation networks: A comprehensive review[END_REF]. Furthermore, there are some interesting proposals to create a basic framework for communicating ABMs results, the most useful of which is the ODD protocol (Overview, Design Concepts, and Details). Based on the work of [START_REF] Grimm | A standard protocol for describing individual-based and agent-based models[END_REF], this protocol is applied to study large-scale system properties that emerge from the adaptive behavior of agents.

Conclusion

Transportation networks are at the center of network analysis in natural and The study of dynamics in transport networks is improved through the application of ABMs. Although such models are relatively new in the field of transport networks, they help to understand endogenous mechanisms that produce unexpected collective behavior in the system. In order to apply ABMs to the analysis of network dynamics, it is important to define the method and mechanism of analysis based on theoretical and empirical evidence. In conclusion, applying complex systems to transport networks is preferred to complement theories and applications in different scientific fields.

A Complex Networks Measures

Global Measure Basic Definition Formula Exponent of the slope for the power-law line drawn y = αx h Hierarchy in a bi-log plot of node frequency over degree distribution Ratio between the observed Transitivity number of closed triplets

C i = λ G (v) τ G (v)
and the maximum possible number of closed triplets in the graph Average Average number of shortest stops between two nodes l G = 1 n(n-1)

= M -1 i jiki[M -1 i 1 2 (ji+ki)] 2 M -1 i 1 2 (j 2 i +k 2 i )-[M -1 i 1 2 (

Figure 1 :

 1 Figure 1: Two main network configurations

  underline the strong relation between actual throughput and degree centrality for container ports. The centrality of cities in air transport networks has received great attention in recent years. The relation between local measures and other local/regional indicators is also the focus of recent research on transportation networks. For instance, Wang et al. (2011) demonstrate the close relationship between the position of Chinese cities in airline networks (i.e. degree, closeness, and betweenness centralities) and their local socio-economic characteristics (i.e. total passenger traffic, urban population, and Gross Regional Product). Analyzing communication networks in the UK, Eagle et al. (2010) find strong interdependencies between the diversity of connections and the economic well-being of localities.

Figure 2 :

 2 Figure 2: Example of accessibility measures. European ports in the road network (Source: adapted from Chapelon (2005)

  gives a more concrete illustration with the evolution of the nodal region of Busan, South Korea based on interport container traffic flows, showing how the hub strategy has resulted in an extension of its tributary area from mostly Japanese satellites to a number of Chinese secondary ports.

Figure 3 :

 3 Figure 3: Example of a nodal region. The case of Busan, South Korea (Source: adapted from Ducruet et al. (2010).

  (a) Generative method displays an unconnected graph (b) A complex patter resulted by connecting or deleting links with small and large distance respectively (c) Degenerative method shows a complete planar graph

Figure 4 :

 4 Figure 4: Generative vs Degenerative Methods

  (a) Transportation network topology with high preference to have shorter links, in this case links less than 0.15. (b) Transportation network topology when it is more desire to have larger links, in this case links bigger than 0.25.

Figure 5 :

 5 Figure 5: Variation in the transportation cost parameter and the emergent transportation network

  (a) Road system of Mexico in 2005 (Source: Instituto Nacional de Estadística y Geografía (INEGI) (2010) (b) Short roads based on a distance less than 10% compare to its maximum.(c) Short roads based on a distance less than 6% and a number of habitants bigger than 10, 000.

Figure 6 :

 6 Figure 6: Application of ABMs to road networks: the case of the road system at Mexico

  social sciences. Recent trends show more interdisciplinary approaches and fast developments of new analytical methods explored by physicists. While transport network analysis still has a lot to gain from such advances, it maintains its specificity by bridging abstract measures and theories with knowledge of the transport industry and broader socio-economic and spatial issues. On another hand, natural sciences give increasing importance to the spatial and social dimension of networks, thereby calling for further collaboration between natural and social sciences. One interesting research path lies in the field of intermodalism, which integrates local studies of planning and strategies with global-level approaches to multiple network topologies and interdependencies. Additionally, a closer look at meso-level measures would certainly foster understanding of how transportation networks are structured and how they evolve with regard to the combination of their local and global properties.

  ji+ki)] 2

	coefficient	nodes at both ends of
		each link (edge) in the
		network
	Local Measure
	Degree	Number of
	centrality	adjacent nodes	k i = C D (i) =		N j x ij
		Number of links needed
	Eccentricity	to reach the most distant	e(x) = max y∈X d(x, y)
		node in the graph
	Shimbel	Sum of the length of
	index	all shortest paths connecting	A i =	N j=1 d ij
		all other nodes in the graph
	Betweenness	Number of times a node
	centrality	is crossed by shortest paths	C B (i) =	g jk (i) g jk
		in the graph
	Average
	nearest	Average degree of adjacent	k nn,i = 1 ki	j α ij k j
	neighbors	nodes
	degree
	Common measures used by complex networks (compiled from various sources)

For a detailed explanation and illustration of graph theoretical measures and indices see: http://people.hofstra.edu/geotrans/eng/ch1en/meth1en/ch1m3en.html

See in-depth reviews of clustering methods by[START_REF] Fortunato | Community detection in graphs[END_REF] and[START_REF] Schaeffer | Graph clustering[END_REF].

For a complete review of literature see[START_REF] Xie | Modeling the growth of transportation networks: A comprehensive review[END_REF].


Koen Vermeir

Dániel Margócsy

Kristie Macrakis

Daniel Juette

Marco Viniegra

Sheila Jasanoff

Michael Herzfeld

Katharine Park

Alisha Rankin

Ken Alder

Liliane Hilaire-Pérez

Adrian Johns

STATES OF SECRECY: AN INTRODUCTION

to appear in the British Journal for the History of Science 'There's not a city in the world without its Loyal and Ancient and Justified and Hermetic Order of little men who think they can reap the secrets of the ancients for a couple of hours every Thursday night and don't realize what prats they look in a robe.' 1

A Science without Secrets 2

 Merton expressed a sentiment shared by many scientists and policy-makers of the age. While military secrecy might have been necessary for developing the bomb and winning World War II, scientific research was supposed to become open again with the advent of peace.

the Office of Scientific Research of Development, famously argued for publishing previously classified, military information, and, as Alex Wellerstein shows in this issue, these feelings were shared by many science policy-makers, if only for a few years. 6 At the level of policy-making, the American enthusiasm for openness was soon supplanted by the security concerns of the McCarthy era. Yet, among sociologists, the views of Merton remained highly influential. 7 Edward Shils emphasized the necessary openness of science in 1956 to argue against McCarthyist obsessions with government secrecy. Knowledge had to be exchanged openly. 'Without it science could not exist.' 8 By 1982, Sissela Bok pointed out the difference between the ritualistic denunciation of secrecy and the actual role of commercial and state secrets in scientific practice. Despite some qualifications about the practical need for secrecy, she concluded with a call for responsibility by the scientists, lest new practices of secrecy 'gain such a strong foothold that they affect the momentum, the quality, and the direction of scientific research in ways difficult to reverse.' 9 But already for Merton too, 'the commercialism of wider society' had a more pernicious effect on science than issues of national security. As he wrote, 'the communism of the scientific ethos [was] abstractly incompatible with the definition of technology as 'private property' in a capitalistic economy.' Guided by this norm, scientific inventors patented 'their work to ensure its being made available for public use,' and not to limit the circulation of knowledge. Across the Atlantic, Michael Polányi echoed these sentiments in his proposal for patent reform in 1944. A staunch opponent of a planned economy of science, Polányi considered the threat of commercial secrecy dire enough to warrant government intervention. The contemporary patent system needed to be replaced by government licensing to support the free circulation of knowledge. 10 For long, the history of science has not paid detailed attention to the implications of Mertonian norms or Bernal's claims, and simply reiterated that modern science is essentially open, while technology is secretive. 11 of the system of courtly patronage, which rewarded scientists for open publication. [START_REF] David | The Historical Origins of 'Open Science': An Essay on Patronage, Reputation and Common Agency Contracting in the Scientific Revolution[END_REF] And, as Joel Mokyr has claimed, the Enlightenment's growing openness and lower access costs to scientific knowledge led to the Industrial Revolution. [START_REF] Mokyr | The Gifts of Athena: Historical Origins of the Knowledge Economy[END_REF] While these current narratives do not neglect the advantages that secrecy might have for individual scientists, they claim that the modern infrastructure of science is distinct by developing powerful incentives for the practice of openness, a public good. 14

Secrets for Profit or Security

Secrecy became a major research topic in the history of science only in the last twenty-five years.

Historians have come to realize how suffused scientific practice is with issues of secrecy. Yet they too often consider secrecy simply as a manner of protecting intellectual property to gain economic or military advantage over competitors. [START_REF]the bibliography up to 1985, that apart from general background literature is divided in two sections 'University/Industry relations' and 'National Security[END_REF] Rational individuals restrict physical and informational access to their knowledge only because they value their property, and governments establish regimes of secrecy only to ensure their military primacy in competition with other states. [START_REF] Hannaway | Laboratory Design and the Aim of Science: Andreas Libavius versus Tycho Brahe[END_REF] Writings in this genre share their interpretive framework with Merton, except that the relative importance of private interests and social norms is inverted. [START_REF]Norms and Counter-Norms in a Select Group of the Apollo Moon Scientists: A Case Study of the Ambivalence of Scientists[END_REF] Some argue that trade secrets and patents help scientists reap financial rewards for their scientific discoveries, and is therefore beneficial for the development of science. Others show that openness is not necessarily a social norm: it can be a rational, individualistic strategy again to maximize private benefits. As a result, the opposition between secretive technology and open science has been qualified, nuanced and contextualized.

As an individual strategy, secrets have received most attention in the context of the early modern scientific revolution. As historians have come to argue for the role of artisans in the development of modern science, they began to explore how craft secrets and trade secrets played a role in early modern practices of knowledge production. Pamela Long famously argued that craft secrets as a category developed only in the late Middle Ages, when guilds in the urbanized centers of Europe first formed a concept of knowledge as intellectual property, and claimed monopoly ownership of it. The picture Long paints is subtle and complex, making distinctions between the rhetoric of openness and actual openness, pointing at differences between individual writers or traditions, and placing them in their practical, intellectual and political context. 18 Writing in a similar vein, Larry Epstein suggested that the invention of craft secrets did not necessarily hamper the development of scientific and artisanal knowledges. Partly proto-intellectual property and partly a form of tacit knowledge, craft secrets provided an incentive for innovation in a world without the modern patent system, and were also disseminated through apprenticeship within existing guild structures, thereby ensuring that knowledge did not become lost after the inventor's death. 19 From the start, however, there also existed incentives for openness, although the early modern patent system was not one of these: as Mario Biagioli argued, patent disclosure became standard practice only after the American and French revolutions. 20 The world of patronage might have acted as a stronger incentive, as artisans frequently touted their skills and knowledges in printed publications to attract potential patrons. 21 Similarly, as science became commercialized during the early modern consumer revolution, the circulation of advertisements and users' guides might have contributed to the larger public's awareness of the importance of technoscientific knowhow. 22 Increasing communication and the overlap between artisan and learned cultures also played a role. 23 By the eighteenth-century, France and other countries in continental Europe offered prizes and organized competitions for artisans who were willing to part with their secrets. 24 The Enlightenment culture of openness (Öffentlichkeit), so eloquently described by Jürgen Habermas, might well have been another incentive for scientific practitioners to abandon secrecy. [START_REF] Habermas | Strukturwandel der Öffentlichkeit: Untersuchungen zu einer Kategorie der Bürgerlichen Gesellschaft[END_REF] Yet Enlightenment openness is usually interpreted in contrast to elite culture and the private sphere rather than to secrecy. The interpretation and translation of Öffentlichkeit as the public sphere, a space of free exchange and critical discussion, has guided recent work in the history of science, which has studied the rise of provincial academies and scientific salons, of professional and amateur journals, of scientific shows and demonstration lectures, of scientific discussions in coffee-houses, clubs, or newspapers, as well as other modes of openness and popularization. [START_REF] Broman | The Habermasian Public Sphere and 'Science in the Enlightenment[END_REF] In this Enlightenment rhetoric, even technological inventions had to be made available to all. The publication of technological secrets took an important place in the French Encyclopédie. Diderot wrote and believed that 'discoveries are only valuable and secure when they circulate among the general mass of people. I am impatient to take them there.' [START_REF] Diderot | The Irresistible Diderot[END_REF] Yet the historiography of the Enlightenment has not fully addressed how to distinguish between the related philosophical and sociological concepts of the private, the elite and the secretive; and the judgment is still out whether Enlightenment openness was a progressive step of modernity, or rather the reconfiguration of traditional cultural and societal boundaries with new forms of privacy and secrecy arising. [START_REF] Horkheimer | Dialectic of Enlightenment[END_REF] Except for the groundbreaking work of Liliane Hilaire-Pérez, the practices of scientific secrecy are relatively understudied for this period. 29 According to Habermas, the public sphere disappeared with the development of a mass consumer culture and the welfare state. The historiography of secrecy in recent science reflects these concurrent developments. Commercialization and increasing competition are often invoked as one explanation for increased secrecy today. A survey has shown that, while in 1966 50% of scientists felt safe about discussing their ongoing work with others, the percentage dropped to 26% by 1998, heralding a new era of entrepreneurial science. [START_REF] Walsh | Secrecy Is Increasing in Step with Competition[END_REF] As Stephen Hilgartner recounts in this issue, scientists operating in the culture of venture capital develop elaborate rituals to communicate results only partially, shirking from the open discussion of unpublished data at scientific conferences lest competing research teams poach their ideas.

At the extreme, some companies might even actively suppress publicly available scientific information relating to their products, and deliberately create doubt and ignorance where consensus had existed before. [START_REF]Agnotology: The Making and Unmaking of Ignorance[END_REF] Secrecy is especially prevalent in the world of modern biotechnology, a discipline at the crossroads of science, technology and the market. [START_REF] Wright | Varieties of Secrets and Secret Varieties: The Case of Biotechnology[END_REF] In the crop industry, for instance, purchasing a genetically modified seed only entitles the owner to plant it, but not to subject it to scientific research. 33 Scientific journals now consider trade secrets and strict confidentiality agreements with employees a standard for modern biotech companies, and offer policy recommendations on how to inculcate the norm of secrecy in openness-oriented academic scientists. [START_REF] Mollman | The Threat from Within: Trade Secret Theft by Employees[END_REF] Yet, as the debates around gene patenting have shown, although commercial considerations and the quest for scientific credit often clash, full secrecy hardly ever emerges. [START_REF] Caulfield | Evidence and Anecdotes: An Analysis of Human Gene Patenting Controversies[END_REF] As Mario Biagioli argues in this issue, scientists at the boundaries of academia and industry do not necessarily seek complete secrecy, but rather the establishment of the proper temporal order of making knowledge public.

Yet, pace Habermas, commercial secrecy is not a new development of consumer society. As historians have shown, the Spanish empire used secrecy as a tool to maintain its monopoly in transatlantic trade already in the sixteenth century; and all colonial empires routinely controlled the movement of skilled practitioners and the flow of information related to maps, trade routes and economic botany. [START_REF] María | Secret Science: Spanish Cosmography and the New World[END_REF] As Vera Keller's article argues in this issue, the secrets of nature were also arcana imperii. What is new for the globalized world of the 20 th and 21 st centuries is the shift of large-scale commercial secrecy from mercantilist states to multinational companies. In this world, the state maintains an interest in technoscientific secrecy primarily as it relates to the military and issues of national security. [START_REF]For a recent overview, see Limiting Knowledge in a Democracy[END_REF] For the historiography, the epitome of this interest is World War II nuclear research. As the Allied Forces came to realize the potential of nuclear fission in 1940, the British and American physicist community voluntarily stopped publishing their results in scientific journals. [START_REF] Aaron | Secrecy and Science Revisited: From Politics to Historical Practice and Back[END_REF] And with the establishment of the Manhattan Project, secrecy was no longer exclusively oriented towards the outside. Most scientific contributors to the bomb worked in isolation from each other, as Lieutenant General Leslie Groves compartmentalized nuclear research, and forbade the open discussion of findings between the different research groups of the Los Alamos community, as well. [START_REF] Norris | Racing for the Bomb: General Leslie R. Groves, the Manhattan project's Indispensable Man[END_REF] The secrecy of nuclear science did not disappear after the dropping of the bomb. During the cold war, a whole culture of secret science emerged in America, with its own, alternative system of authorship, peer review and classified journals. [START_REF] Gusterson | People of the Bomb: Portraits of America's Nuclear Complex[END_REF] The big science of physics was studied in the two separate spheres of the academia and the military, at times leading to discovering the same results twice. And the situation barely improved with the fall of the Berlin wall. After the promise of declassification in the mid-1990s, government secrecy has gained new powers in the years since 9/11. Within the United States, the government spent almost nine billion dollars on security classification in 2009, a sum that has doubled since 1995. 41 Many of these investments actually fund commercial enterprises, as the U.S. government has been outsourcing much of its intelligence operations to private companies. [START_REF] Shorrock | Spies for Hire: The Secret World of Intelligence Outsourcing[END_REF] While the defenders of these policies claim that the culture of secrecy is only for the protection of sensitive information and vulnerable populations, opponents are eager to point out the dangers of the existence of a classified universe on par with the public world. [START_REF] Galison | Removing Knowledge[END_REF] According to these critics, the contemporary culture of secrecy is not only about the protection of intellectual property, for commercial or military reasons, but it has a self-sustaining power on its own. It creates hierarchies of inclusion and exclusion, and leads to a government that is only responsible to itself. From the understanding of secrets as protected intellectual property, we have arrived in the world of secrecy.

From Secrets to Secrecy

The prevalence of craft secrecy, intellectual property and state secrets in the historiography of science betrays a focus on the content of the secret, i.e. on the invention, method or recipe that people wanted to keep hidden for security reasons or for making a profit. With this special issue, we want to give a state of research on scientific secrecy, but we also want also to hint at the richness of historiographical work still to be done when the focus is shifted from secrets to secrecy as a dynamic social relation. In many instances, what is kept secret is not even relevant for studying the dynamics of secrecy, i.e. the practices of simulation and dissimulation, the rhetoric of secretiveness or the strategies of hiding and revealing that are employed.

The work of the German sociologist Georg Simmel is a rich source of inspiration for studying the dynamics of secrecy. For him, secrecy, 'one of the greatest accomplishments of humanity,' was a necessary element of human society. 44 Just as the circulation and exchange of gifts, the non-circulation and withholding of knowledge was a structuring force of society and social hierarchy. While Simmel's work had a large influence in anthropology and religious studies, it has had virtually no impact on the historiography of science. This is unfortunate, because understanding the socio-psychology and sociology of secrecy are crucial for the study of the practices of secrecy in the history of science, as Koen Vermeir argues in this issue. At least, such a study will help us to overcome too simplistic, monolithic and reductionist definitions of secrecy and openness.

Secrets evoke excitement and desire. Those who have a secret are under constant tension: they want to keep the secret, but they also want to indicate that they have a secret, to veil and unveil it at the same time. Those who have a secret are teased and tempted into betraying the secret, and reach release and climax only when the secret is shared with others. 45 At the same time, those who know about the secret but do not have it, desire it. They project their hopes and fears on it, and these passions are heightened by the unknown character of the secret. In these scenarios, owners do not use secrets as a tool of monopoly, but rather to establish a special bond with those to whom they reveal their knowledge. 46 Such a desire to share could also lead to open publication: one example is that after the invention of the printing press, books of secrets were among the most popular and most widely published. 47 This seems incomprehensible when 44 Georg Simmel, 'The Sociology of Secrecy and of Secret Societies', The American Journal of Sociology (1906) 11, pp. 441-498, especially p. 462. 45 Simmel also describes the 'joy of confession', for instance, 'which may contain that sense of power in negative and perverted form, as self-abasement and contrition.' Simmel, op.cit., p. 466. 46 See, for instance, Giorgio Vasari's account of Jan van Eyck's sharing of his secret of oil painting. Marc Gotlieb, 'The Painter's Secret: Invention and Rivalry from Vasari to Balzac', Art Bulletin (2002) 84, pp. 469-490; Michael Herzfeld, op. cit., ch. 4. 47 For these books of secrets, see Eamon, op. cit., who is more sensitive than most to the dynamics of secrecy but does not historians stay focused on the knowledge content of the secret instead of on the psychodynamics of secrecy.

As objects of desire, secrets accrue a special value, even if their content would in itself be valueless.

They hide the real value of the content by keeping it hidden. Blacked out spaces in texts, empty spaces on maps or even neurotic symptoms indicate that something has been intentionally hidden, and sparks speculation about its extraordinary value or meaning. [START_REF] Harley | Silences and Secrecy: The Hidden Agenda of Cartography in Early Modern Europe[END_REF] Secrecy can thus be used to support beliefs that are mediocre, irrational, unverifiable and nonsensical, in science as well as in esoteric practices. 49 Therefore, alchemists or professors of secrets were often disappointed when they exchanged secrets and assessed the real value of what they received in return. Yet, in other cases, the mark of secrecy serves as a correct indicator of the object's importance. In 1941, for instance, the Russian scientists Georgii Flerov inferred that an atomic bomb might be possible to build by noticing the suddenly secretive behaviour of American researchers about nuclear physics, and wrote that 'a stamp of silence has been laid on this question, and this is the best sign of what kind of burning work is going on right now.' 50 A proper assessment of a secret's value is especially complicated when the classified information is publicly available elsewhere. 51 As recent events have shown, government censorship and secrecy are often about maintaining the illusion of information control even when everyone is able to download classified documents from a website. The concept of censorship can also be used to undo the dichotomy of private and public, and the inner and the outer, as Peter Galison makes clear in this issue. Drawing on contemporary practices of censorship, Freud could even interpret forgetting as secrecy, as the intentional concealment by an hidden internal censor.

Secrecy is essential to our internal lives, we cannot even trust ourselves. [START_REF]For a similar perspective, see Miklós Haraszti, The Velvet Prison: Artists under State Socialism[END_REF] Objects of desire and bearers of value, secrets are a social phenomenon. Indeed, as Stephen Hilgartner points out in this issue, secrecy is often a performance aimed at an audience. For example, when Greek shepherds shared their secret stories of animal theft with anthropologist Michael Herzfeld, they acted as if they were approaching him surreptitiously, but in fact also ensured that their fellow shepherds noticed and appreciated this secretive behavior. [START_REF] Herzfeld | The Body Impolitic: Artisans and Artifice in the Global Hierarchy of Value[END_REF] In a similar vein, sociologists argue that secrecy can make as well as break a social group. According to Simmel, a child gets a sense of an 'I' when it becomes aware spend much explicit reflection on it.

that it can keep secrets from others, and then, those who share a secret amongst each other become a 'we'.

Secrecy has a strong effect on practices of inclusion, exclusion and group formation. The internal role of secrecy is to elicit reciprocal confidence and trust, a group identity and a tight community. Pythagoreans even did a mock burial of those who betrayed their secrets, and afterwards ignored these expelled initiates as if they were dead. An even stronger bond arises when a secret about the 'we' is mutually created, as with accomplices in a shared crime, or a shared ritual. This bond will be the stronger if this secret is socially unsupported and cannot be revealed by one of the participants without risk for himself. [START_REF] In | Secrecy and Status: The Social Construction of Forbidden Relationships[END_REF] Understood as a tool of group formation, secrecy is also about control and the establishment of hierarchies. Having a secret gives one a strong sense of identity, and also control over others. It binds those with whom you share the secret to you, morally and psychologically. Furthermore, the possibility of disclosing the secret gives everyone power over the group as a whole. A different kind of power arises when one does not only know more than the others, but one also knows that one knows more. This means that the others' desire for the secret or for being part of the group can be manipulated and exploited.

Secrecy gives rise to strong feelings, of freedom (you are special) but also of frustration and oppression (you cannot be like the others, or you are part of a group closely-knit around the secret). Secrecy is a way of controlling the interface between what is kept hidden and what is made public, the access to information, and who knows what. Secrecy is often hierarchically structured: some know more than others, in different gradations, and these levels of knowing are closely guarded. [START_REF] Ku | Boundary Politics in the Public Sphere: Openness, Secrecy, and Leak[END_REF] Such practices of secrecy are prevalent both in social networks and in formal institutions. As Max Weber has shown at the institutional level, secrecy plays a central role in bureaucracies. From combining Weber's and Simmel's arguments, it follows that the current bureaucracy of scientific and governmental institutions can fruitfully be analysed according to the social structures of secrecy, with different levels of access to and control of information. [START_REF] Weber | Essays in Sociology[END_REF] Yet looser networks similarly operate by the establishment of a differential access to secrets. As Lawrence Principe and William Newman have shown, when the alchemist George Starkey communicated his alchemical secrets to Robert Boyle and not to other alchemists, this was a marker of their relationship in which trust, respect, class and patronage played an essential role. [START_REF] Newman | We could also imagine the scientist as a trickster, trying to outwit nature by his cunning practices. The aim of our dynamic analysis of secrecy, of the play of veiling and revealing[END_REF] Secrets could establish even long chains of relationships. When Henry Oldenburg sent a secret to Samuel Hartlib, for instance, he instructed the recipient to only tell it to Robert Boyle, who surely would only communicate it to Lady Ranalaugh, who also knew how to keep a secret. Secrecy here is a social marker that defines sometimes highly specific social relationships, including those that govern scientific exchanges. 58 Viewed as a social phenomenon, secrecy can have very real effects, negatively, in hampering circulation, in creating distrust and making social relations crumble. Yet it also has more positive psychological and social effects. As Tanya Luhrman has argued, sharing secrets within a closed group can also have therapeutic, sometimes even psychosomatic, effects -hence the attraction of many esoteric communities. 59 Secrecy can also accrue deeper, symbolical powers, and it can become the focus of ritualized practices. The mystery around secrets gives their bearers an aura of superiority. Secrets can be icons and indices of political power, used for controlling people. Secrets can even be transcendent, as divine secrets, at the core of religious mysteries or of the book of nature, only partially revealed to the faithful. In the end, the meaning of secrets is overdetermined, because they are hidden, and this allows diverse political, religious and other meanings and values to be projected on them. 60 Of course, this dynamical analysis of secrecy has to be historicised in order to better understand the historical specificity and conditions of specific instances of secrecy, with due attention to actors' categories and the richness of the practices involved. In this issue, we put forward an analysis of secrecy that goes beyond its usual understanding as the rational behaviour of an opportunist scientist or state interested in maintaining their intellectual property. Such an analysis allows us to place secrecy in a broader

  As economic historians have argued, this openness might have been the result of the institutional framework of early modern Europe. Paul David suggested that modern science was born out Science, the Endless Frontier: A Report to the President, Washington: US Government Printing Office, 1945. See also Michael Aaron Dennis, 'Reconstructing Sociotechnical Order: Vannevar Bush and US Science Policy', in Sheila Jasanoff (ed.), States of Knowledge: The Co-Production of Science and Social Order, London: Routledge, 2004, pp. 225-253. Secrets: On the Ethics of Concealment and Revelation, New York: Vintage, 1989, p. 170. For an updated but similar view, see David Resnik, 'Openness versus Secrecy in Scientific Research', Episteme (2006) 2, pp. 135-147.

	7	Steven Shapin, The Scientific Life: A Moral History of a Late Modern Vocation, Chicago: University of Chicago Press, 2008, pp.
		113-115.
	8	Edward Shils, The Torment of Secrecy: The Background and Consequences of American Security Policies, Glencoe: The Free Press, 1956,
		p. 176.

6 Vannevar Bush, 9 Sissela Bok, 10 Michael Polányi, 'Patent Reform', The Review of Economic Studies (1944) 11, pp. 61-76; Adrian Johns, 'Intellectual Property and the Nature of Science', Cultural Studies (2006) 20, pp. 145-164. 11 H. Floris Cohen, The Scientific Revolution: A Historiographic Inquiry, Chicago: University of Chicago Press, 1994, pp. 200-204; Derek de Solla Price, Science since Babylon, New Haven: Yale University Press, 1961, pp. 117-35; McMullin, op.cit.; David Hull, 'Openness and Secrecy in Science: Their Origins and Limitationsm', Science, Technology & Human Values (1985) 10, pp. 4-13.

  Liliane Hilaire-Pérez and Catherine Verna, 'Dissemination of Technical Knowledge in the Middle Ages and the Early Modern Era: New Approaches and Methodological Issues', Technology and Culture (2006) 47, pp. 536-565; Mario Biagioli, 'Patent Republic: Specifying Inventions, Constructing Authors and Rights', Social Research (2003) 7, pp. 1129-1172. 21 Long, op. cit., ch. 4; Marcus Popplow, 'Why Draw Pictures of Machines? The Social Contexts of Early Modern Machine Drawings', in Wolfgang Lefèvre, ed. Picturing Machines 1400-1700, Cambridge: MIT Press, 2004, pp. 17-48; Paul David, op. cit.

18 Pamela Long, Openness, Secrecy, Authorship: Technical Arts and the Culture of Knowledge from Antiquity to the Renaissance, Baltimore; Johns Hopkins University Press, 2001. In a special issue edited by Karel Davids, a number of scholars have elaborated on the tradition of craft secrets. See Karel Davids (ed.), Early Science and Medicine (2005) 10, and especially Karel Davids, 'Craft Secrecy in Europe in the Early Modern Period: A Comparative View', Early Science and Medicine (2005) 10, pp. 341-348. 19 Stephan Epstein, 'Craft Guilds. Apprenticeship and Technological Change in Preindustrial Europe', The Journal of Economic History (1998) 29, pp. 684-713.; Stephan Epstein and Maarten Prak, 'Introduction: Guilds, Innovation, and the European Economy, 1400-1800', in Stephan Epstein and Maarten Prak, eds. Guilds, Innovation, and the European Economy, 1400-1800, Cambridge: Cambridge University Press, 2008, pp. 1-24. 20 22 Daniel Margócsy, 'Advertising Cadavers in the Republic of Letters: Anatomical Publications in Early Modern Netherlands', British Journal for the History of Science (2009) 42, pp. 187-210; Liliane Hilaire-Pérez and Marie Thébaud-Sorger, 'Les techniques dans l'espace public. Publicité des inventions et littérature d'usage au XVIII e siècle (France, Angleterre)', Revue de Synthèse (2006) 127, pp. 393-428. 23 Long, op. cit., ch 6-7. 24 Liliane Hilaire-Pérez, L'invention technique au siècle des Lumières, Paris: Albin Michel, 2000.