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Knowledge diffusion in the network of international business travel

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We use aggregated and anonymized information based on international expenditures through corporate payment cards to map the network of global business travel. We combine this network with information on the industrial composition and export baskets of national economies. The business travel network helps to predict which economic activities will grow in a country, which new activities will develop and which old activities will be abandoned. In statistical terms, business travel has the most substantial impact among a range of bilateral relationships between countries, such as trade, foreign direct investments and migration. Moreover, our analysis suggests that this impact is causal: business travel from countries specializing in a specific industry causes growth in that economic activity in the destination country. Our interpretation of this is that business travel helps to diffuse knowledge, and we use our estimates to assess which countries contribute or benefit the most from the diffusion of knowledge through global business travel. Coscia et al. use a large dataset of business travel to show that, before they become competitive in new industries, countries receive visitors from places where that industry already thrives.
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1Center for International Development, Harvard University, Cambridge, MA, USA. 2ITU Copenhagen, Copenhagen, Denmark. 3Mastercard Center for
Inclusive Growth, Purchase, NY, USA. e-mail:
Globalization has led to a tremendous increase in interna-
tional business travel. The growth of international business
travel has outstripped the growth of the world economy by a
wide margin; whereas nominal, US$-denominated, global GDP has
risen by 0.7% per year between 2011 and 2016 (ref. 1), our data sug-
gest that nominal expenditures related to business travel have grown
at an annualized rate of 8.3% over the same period (see Methods).
This growth coincides with substantial improvements in the avail-
ability, quality and costs of long-distance communication technolo-
gies. From conference calls to online collaboration platforms, new
technologies have made it easier for businesses to connect across
the globe without the need for costly and time-consuming travel.
So why do we still need to travel so much? What can face-to-face
interaction on business trips achieve that other means of commu-
nication cannot?
Business scholars have argued that, without face-to-face com-
munication, some knowledge is hard to transmit2. Accordingly,
one can think of knowledge as consisting of three components. The
first component is knowledge that is codified in production reci-
pes, algorithms, textbooks, blueprints and so on3. This knowledge
component consists of know-what and know-why4—knowledge
about facts, such as the physical dimensions of a product, and about
well-understood causal mechanisms, such as the laws of physics.
The second component consists of knowledge that is embedded in
physical artifacts, such as machines, tools or intermediate products5.
Nowadays, either component can be easily transferred. The costs at
which machines, tools and semi-finished products can be shipped
has never been lower and, with internet-based technology, code and
textbooks can be transmitted almost instantaneously at high fidelity.
However, transmitting the third component of knowledge
is more complex. This component consists of knowing how to
expertly carry out certain tasks oneself or knowing where to find
someone who possesses this expertise. The former is often referred
to as know-how and the latter as know-who4. This third component
of knowledge resides in people, teams of people and in the relation-
ships between these teams6. Large parts of such knowledge cannot
be easily articulated by its carriers, and it would be extremely costly
to codify7, let alone transmitted in digital form; it has therefore been
described as tacit8. Although tacit knowledge is typically associ-
ated with physical and artisanal skills, sociologists of science have
shown that tacit knowledge also has an important role in science
and technology9,10. Moreover, its transfer, which is indispensable in
the training of scientists and engineers, typically involves repeated
interaction, imitation and on-the-job training and is often orga-
nized in an apprenticeship-like relationship between the experi-
enced scientist and the trainee, as evident in common practice in
doctoral training programs and artfully illustrated by MacKenzie
and Spinardi’s11 case study on the design of nuclear weapons.
Given that, in modern economies, knowledge about even the
most mundane production technologies is too complex for any sin-
gle individual to comprehend in full, know-how often needs to be
complemented by know-who, that is, knowledge of how and where
to access experts in a field. Know-who is particularly important in
interfirm relationships, as it helps to identify and forge alliances
with customers and suppliers12. Similar to know-how, know-who
tends to be more tacit, embedded in people’s understanding of the
social network that surrounds them as well as in the understanding
of where in this network reliable and trustworthy expertise can be
In light of this, one plausible explanation for why business travel
has not just endured, but even expanded, despite the increasing
availability of substitutes in the form of new communication tech-
nologies is that these new technologies are still inadequate when
it comes to transmitting tacit know-how or to establishing the
trust-based relationships associated with know-who. By temporar-
ily relocating know-how by moving the individuals who carry it,
business travel enables face-to-face contacts through which tacit
know-how can diffuse and trust and social networks can develop.
Here we mapped the pattern of global business travel and investi-
gated how it affects the growth of economic activity. Our underlying
hypothesis is that business travel enables the diffusion of know-how
and know-who across countries. Our hypothesis is related to pre-
vious research that has used business travel as an explanation for
bilateral trade links13,14, innovation15 and increases in productivity16.
Knowledge diffusion in the network of
international business travel
Michele Coscia 1,2 ✉ , Frank M. H. Neffke 1 and Ricardo Hausmann 1,3
We use aggregated and anonymized information based on international expenditures through corporate payment cards to map
the network of global business travel. We combine this network with information on the industrial composition and export
baskets of national economies. The business travel network helps to predict which economic activities will grow in a country,
which new activities will develop and which old activities will be abandoned. In statistical terms, business travel has the most
substantial impact among a range of bilateral relationships between countries, such as trade, foreign direct investments and
migration. Moreover, our analysis suggests that this impact is causal: business travel from countries specializing in a specific
industry causes growth in that economic activity in the destination country. Our interpretation of this is that business travel
helps to diffuse knowledge, and we use our estimates to assess which countries contribute or benefit the most from the diffu-
sion of knowledge through global business travel.
NATURE HUMAN BEHAVIOUR | VOL 4 | OCTOBER 2020 | 1011–1020 | 1011
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... More recently, some studies have shown the importance of removing random or weak edges to reveal an underlying structure (i.e., the backbone) in networks formed by co-interactions [104,174,55,54,182,183,123,127]. For example, Leão et al. showed how removing random edges in co-authoring networks converge to a topology with more pure social relationships and better quality community structures, compared to the original complete network [148]. ...
... Starting with applications, multiple works in various őelds have shown the importance of backbone extraction methods to deal with random, sporadic, and weak edges that may obfuscate the phenomenon under study. For example, several studies applied early proposed methods to study phenomena in biological networks [225,30], transportation networks [316,60], economic networks [201,182,183], co-authoring networks [148,89], human mobility networks [274,54,29] as well as congressional voting networks [32]. More recently, some studies have highlighted the importance of this task in social media applications [218,1,220,221]. ...
... With respect to the former, we note that user interactions when commenting on various topics have already been studied in the context of other social media applications such as Twitter [173] and Facebook [278,291]. As for the latter, one can cite mobility networks [54,29] and biological networks [236], where some of the methods we use here have been applied without careful investigation. ...
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Understanding the collective behavior of (groups of) individuals in complex systems, even in scenarios where the individual properties of their components are known, is a challenge. From the point of view of network models, the collective actions of these individuals are often projected on a graph forming a network of co-interactions, which we here refer to as a many-to-many network. However, the volume and diversity with which these co-interactions are observed in the most varied systems, such as, for example, social media platforms, economic transactions and political behavior in voting systems, impose challenges in the extraction of patterns (structural, contextual and temporal) emerging from collective behavior and that are fundamentally related to a phenomenon under study. Specifically, the frequent presence of a large number of weak and sporadic co-interactions, which, therefore, do not necessarily reflect patterns related to the phenomenon of interest, end up introducing noise to the network model. The large amount of noise, in turn, may obfuscate the most fundamental behavior patterns captured by the network model, that is, the patterns that are essentially relevant to the understanding of the phenomenon under investigation. Removing such noise becomes then a key challenge. Our goal in this dissertation is to investigate the modeling and analysis of collective behavior patterns that emerge in networks formed by co-interactions in different contexts, aiming to extract relevant and fundamental information about a target phenomenon of interest. Specifically, we tackle the extraction of structural, contextual and temporal properties associated with patterns of collective behavior that are fundamentally represented by communities extracted from the network. To this end, we propose a general strategy that addresses the aforementioned challenges. In particular, this strategy includes, as an initial step, the identification and extraction of the network backbone, that is, the subset of the edges that are indeed relevant to the target study. The next steps consist of the extraction of communities from this backbone as a manifestation of the existing collective behavior patterns and the characterization of the structural (topological), contextual (related to the phenomenon of interest) and temporal (dynamic) properties of these communities. Based on this general strategy, we propose specific artifacts for some of the steps that compose it and advance the state-of-the-art, in particular with a new method for backbone extraction, a new temporal node embedding method capable of representing and extracting different temporal patterns of interest from a sequence of networks, and finally a methodology to support the selection and evaluation of backbones from a structural and contextual point of view, considering the most common scenario where there is no ground truth. Furthermore, we explore these artifacts by studying three different phenomena that require different modeling and analysis strategies. Specifically, we investigate: (i) the formation of ideological groups in the Brazilian and U.S. House of Representatives, (ii) online discussions on Instagram in Brazil and Italy, and (iii) information dissemination on WhatsApp. Overall, our results show that the proposed artifacts offer relevant contributions to the field in which this dissertation is inserted.
... In this sense, know-how differs from conventional knowledge. Whereas knowledge can be codified as 'building-blocks' of the 'know-what', know-how refers to the practices and routines required to successfully utilize these building blocks to perform specific tasks (Coscia et al., 2020;Hausmann & Neffke, 2019). For example, having knowledge of certain aspects of the bicycle, such as gravity or rolling resistance, is insufficient to ride a bicycle. ...
... The production of knowledge and know-how is increasingly the outcome of teamwork (Jones et al., 2008;Leahey, 2016;van der Wouden, 2020;Wuchty et al., 2007). As the production of knowledge is key to the competitiveness of firms (Coscia et al., 2020;Lissoni, 2001;Teece, 1998;von Hippel, 1989;Von Hippel, 1994), the organization and structure of team-work has strategic importance (Chuang et al., 2013). This raises questions on how team composition in terms of gender (Hansen et al., 2006;Nielsen et al., 2017;Woolley & Malone, 2011), hierarchy (Jones et al., 2008), status (Groysberg et al., 2010), team-size (Wu et al., 2019), knowledge diversity (Pollok et al., 2021), network structure (Guimerà et al., 2005), language (Gibson & Zellmer-Bruhn, 2001), trust (Zaheer et al., 1998) and geographical distance (Catalini et al., 2020;Espinosa et al., 2007;Salazar Miranda & Claudel, 2021;Yang et al., 2022) affect performance. ...
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There is little question that new communication and transportation technologies have effectively “shrunk the world” for a great many activities. At the same time, the “death of distance” has been greatly exaggerated, especially in fields such as academic scholarship and knowledge development where the positive benefits of knowledge spillovers remain highly distance dependent. We analyze 17.6 million publications authored by 1.7 million scholars to examine how knowledge spillovers between scholars collaborating at different geographical distances impacts their future knowledge portfolios. Our results show that in 1975, scholars collaborating locally were 57 % more likely to learn from knowledge spillovers than similar scholars collaborating non-locally. We identify four factors that structure these findings. Individuals deriving the greatest learning premiums from local collaboration tend to be (1) in earlier stages of their career; (2) associated with lower-ranked institutions; (3) working with fewer collaborators; and (4) in STEM fields. The probability of learning drops with geographical distance and correspond to the number of institutional boundaries crossed during collaboration. We conclude that even in the 21st century, geographical distance still negatively impacts knowledge spillovers through collaboration. These findings have implications for debates in innovation and management studies concerning knowledge spillovers, the spatial organization of (knowledge-intensive) economic activity, regional innovation policies, structuring team-work and working-from-home vs. returning to office.
... Сейчас удельный вес каждого из этих видов глобального обмена не только меняется в абсолютном выражении, но и относительно друг друга. Так, по расчетам Кошиа и др., постоянное прекращение международных деловых поездок сократит глобальный валовой продукт на поразительные 17 % из-за затруднения потоков знаний через границы [1]. Бракман и др. ...
... 1. Огромное наращивание предупредительных запасов: для крупнейших 3000 фирм во всем мире они выросли с 6 % до 9 % мирового ВВП с 2016 г. 1 . Многие фирмы принимают двойные источники поставок одних и тех же материалов и долгосрочные контракты. ...
The COVID-19 pandemic and the subsequent politicization of the solution of economic problems have created a new economic reality. This article is devoted to the consideration of the possibilities and problems of the formation of a new international division of labor. The scientific and practical works of Russian and foreign scientists in the field of the international division of labor and global trade were used as the methodological basis of the study. The division in the analysis into the short term and the long term is necessary to study the sequence of steps in the formation of a new international division of labor. The author identifies the following manifestations of the new international division of labor: the crisis of "supply chains", the deformation of the global and national labor markets, the politicization of the solution of economic issues at the international level, the significant lag of economic theory from real business processes, the impact of climate change on the formation of a new international division of labor and the transition from a "free trade" policy to a "fair trade" policy. The "supply chain" crisis has shown that the order of supply in international trade, based on the "just in time" system, could not withstand the external shock, causing shortages and inflation. The deformation of labor markets is manifested in COVID-motivated automation, in the "migration trap", in the increased polarization of employment. The countries of the “golden billion”, having been defeated in competition in the global market, are striving for a political solution to economic problems. The EU is trying to play the game of European super monopoly and super monopsony. The possibility of a political solution to the problems of global economic competition is based on the fact that changes in the real economy that have taken place in recent decades have not adequately changed the international financial sphere. Regardless of the current geopolitical situation, in the long term, the expansion of the use of new technologies and artificial intelligence creates the basis for the international division of labor of the next level. To maintain and potentially improve its place in the international division of labor of the Russian Federation, a consistent transition from the “inertial” to the “innovative” path of development is necessary.
... In contrast, de Bok and van Oort (2011) found no significant results for firms in the manufacturing sector and suspect a trade-off between accessibility and the cost of rent since these firms require more space to do business. Coscia et al. (2020) found that business trips decline with travel-time distance, hence why APS firms heavily depend on transportation infrastructures to facilitate face-to-face business activities. For these firms, airports are seen as nodes in a global network of firm locations. ...
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This paper analyzes how positional and relational data in 186 regions of Germany influence the location choices of knowledge-based firms. Where firms locate depends on specific local and interconnected resources, which are unevenly distributed in space. This paper presents an innovative way to study such firm location decisions through network analysis that relates exponential random graph modeling (ERGM) to the interlocking network model (INM). By combining attribute and relational data into a comprehensive dataset, we capture both the spatial point characteristics and the relationships between locations. Our approach departs from the general description of individual location decisions in cities and puts extensive networks of knowledge-intensive firms at the center of inquiry. This method can therefore be used to investigate the individual importance of accessibility and supra-local connectivity in firm networks. We use attributional data for transport (rail, air), universities, and population, each on a functional regional level; we use relational data for travel time (rail, road, air) and frequency of relations (rail, air) between two regions. The 186 functional regions are assigned to a three-level grade of urbanization, while knowledge-intensive economic activities are grouped into four knowledge bases. This research is vital to understand further the network structure under which firms choose locations. The results indicate that spatial features, such as the population of or universities in a region, seem to be favorable but also reveal distinct differences, i.e., the proximity to transport infrastructure and different valuations for accessibility for each knowledge base.
... The movement of individuals is fundamental to our society (Schläpfer et al., 2021) and an essential component to understanding many noticeable societal challenges, such as urbanization (Barthelemy, 2019), urban activity (Ahas et al., 2015) and epidemic spread (Kraemer et al., 2020). Central to the study of human mobility are the daily visited locations, where meaningful activities (such as working and shopping) are conducted , and where individuals might interact to exchange knowledge (Coscia et al., 2020). The ability to correctly predict the next activity location an individual will reach is required for many downstream tasks, such as personalized recommendation systems (Sánchez and Bellogín, 2022), traffic optimization (Rossi et al., 2020), mobile communication networks (Zhang and Dai, 2019), and sustainable transportation systems (Ma and Zhang, 2022). ...
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Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual's next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations' land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model's performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.
... Likewise, MNCs or FDIs have been acknowledged to playing such transformative role, for example in the context of Hungary, where MNCs favoured a process of unrelated diversification (Elekes et al., 2019). Other recent studies point to the importance of temporary proximity for knowledge diffusion, for example via business trips (Coscia et al., 2020). ...
... Starting with applications, multiple works in various fields have shown the importance of backbone extraction methods to deal with random, sporadic, and weak edges that may obfuscate the phenomenon under study. For example, several studies applied early proposed methods to study phenomena in biological networks [59,60], economic networks [61][62][63], coauthoring networks [64,65], human mobility networks [66][67][68] as well as congressional voting networks [25,69,70]. ...
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Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions , represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.
Der hybride, räumlich-verteilte Kongress „BOCOM – Experience Borderless Communication“ am 3. September 2020 nutzte innovative Technologien, um Referent*innen und Teilnehmer*innen aus aller Welt zu einer globalen Event-Community zu verbinden. Die Teilnahme war sowohl physisch in mehreren dezentralen Hubs möglich als auch virtuell über einen Livestream.
Europe experienced a dramatic and rapid drop of mobility in 2020 due to the COVID-19 pandemic during the different lockdowns periods. The impact was immediate and severe for long-distance travel in particular for the air transport industry. This crisis may also have long-term implications for the travel industry notably for business trips with the adoption of new ways of working. In this article, we point out the resilience of travel behaviors since we suggest that the pandemic is more likely to accelerate preexisting trends. Using time series data from Eurostat, we observe a preexisting decreasing trend for corporate travel. Data also suggest that flygskam is gaining Europe since 2018 with a slight reduction of air transport demand. These figures shed light on the near future of mobility in Europe. Leisure travel will be essential for operators’ recovery, opening up new business opportunities for rail transport (night trains, low-cost, etc.).
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We investigate whether labor mobility can be a distinct source of growth by studying the productivity impact of business visits (BVs), vis-à-vis that of other well-known drivers of productivity enhancement. Our analysis uses an unbalanced panel—covering on average 16 sectors per year in ten countries during the period 1998–2011—which combines unique and novel data on BVs sourced from the US National Business Travel Association with Organization for Economic Cooperation and Development (OECD) data on R&D and capital formation. We find that mobility through BVs is an effective mechanism to improve productivity, being about half that obtained by investing in R&D. This relevant finding invites viewing short-term mobility as a strategic mechanism and prospective policy tool to overcome productivity slowdowns and foster economic growth.
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Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics and social sciences. In human activities, Zipf-laws describe for example the frequency of words appearance in a text or the purchases types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a new framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchases sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.
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Partial financial support has been received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under projects MODASS (FIS2011-24785) and INTENSE@COSYP (FIS2012-30634), and from the EU Commission through projects EUNOIA, LASAGNE and INSIGHT. The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educacion, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017. JJR acknowledges funding from the Ramon y Cajal program of MINECO.
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Scientific studies of society increasingly rely on digital traces produced by various aspects of human activity. In this paper, we use a relatively unexplored source of data, anonymized records of bank card transactions collected in Spain by a big European bank, in order to propose a new classification scheme of cities based on the economic behavior of their residents. First, we study how individual spending behavior is qualitatively and quantitatively affected by various factors such as customer's age, gender, and size of a home city. We show that, similar to other socioeconomic urban quantities, individual spending activity exhibits a statistically significant superlinear scaling with city size. With respect to the general trends, we quantify the distinctive signature of each city in terms of residents' spending behavior, independently from the effects of scale and demographic heterogeneity. Based on the comparison of city signatures, we build a novel classification of cities across Spain in three categories. That classification is, with few exceptions, stable over different ways of city definition and connects with a meaningful socioeconomic interpretation. Furthermore, it appears to be related with the ability of cities to attract foreign visitors, which is a particularly remarkable finding given that the classification was based exclusively on the behavioral patterns of city residents. This highlights the far-reaching applicability of the presented classification approach and its ability to discover patterns that go beyond the quantities directly involved in it.
Networks are powerful instruments to study complex phenomena, but they become hard to analyze in data that contain noise. Network backbones provide a tool to extract the latent structure from noisy networks by pruning non-salient edges. We describe a new approach to extract such backbones. We assume that edge weights are drawn from a binomial distribution, and estimate the error-variance in edge weights using a Bayesian framework. Our approach uses a more realistic null model for the edge weight creation process than prior work. In particular, it simultaneously considers the propensity of nodes to send and receive connections, whereas previous approaches only considered nodes as emitters of edges. We test our model with real world networks of different types (flows, stocks, co-occurrences, directed, undirected) and show that our Noise-Corrected approach returns backbones that outperform other approaches on a number of criteria. Our approach is scalable, able to deal with networks with millions of edges.
This paper starts with a critical assessment of the recent paper by Cowan, Foray and David. It also provides the authors' own assessment of why the tacit-codified distinction is important in relation to economic analysis and knowledge management practice. The criticism of Cowan, Foray and David centres on three points. Firstly, it is argued that the discussion on codification must make the fundamental distinction between knowledge about the world (know-what) and knowledge in the form of skills and competence (know-how). Secondly, it is argued that the dichotomy between codifiable and non-codifiable knowledge is problematic since it is rare that a body of knowledge can be completely transformed into codified form without losing some of its original characteristics and that most forms of relevant knowledge are mixed in these respects. Thirdly, we contest their implicit assumption that codification always represents progress. We conclude that for these reasons their intellectual exercise of extending definitions of what is codified and possible to codify, while in principle addressing very important issues related to innovation policy and knowledge management, ends up having limited practical implications for these areas. Copyright 2002, Oxford University Press.
In the very successful and widely discussed first volume in the Golem series, The Golem: What You Should Know about Science, Harry Collins and Trevor Pinch likened science to the Golem, a creature from Jewish mythology, a powerful creature which, while not evil, can be dangerous because it is clumsy. in this second volume, the authors now consider the Golem of technology. in a series of case studies they demonstrate that the imperfections in technology are related to the uncertainties in science described in the first volume. The case studies cover the role of the Patriot anti-missile missile in the Gulf War, the Challenger space shuttle explosion, tests of nuclear fuel flasks and of anti-misting kerosene as a fuel for airplanes, economic modeling, the question of the origins of oil, analysis of the Chernobyl nuclear disaster, and the contribution of lay expertise to the analysis of treatments for AIDS.
This paper attempts a greater precision and clarity of understanding concerning the nature and economic significance of knowledge and its variegated forms by presenting 'the skeptical economist's guide to 'tacit knowledge''. It critically reconsiders the ways in which the concepts of tacitness and codification have come to be employed by economists and develops a more coherent re-conceptualization of these aspects of knowledge production and distribution activities. It seeks also to show that a proposed alternative framework for the study of knowledge codification activities offers a more useful guide for further research directed to informing public policies for science, technological innovation and long-run economic growth.