Three Essays on Methodologies for Dynamic Modeling of Emerging Socio-technical Systems

Mohammad Amin Dehdarian
EPFL, 2017
EPFL thesis

Abstract

Socio-technical energy transitions are long-term and major transformations in incumbent energy infrastructures. They include fundamental changes in technologies as well as institutions and social patterns. Transition studies are primarily focused on frameworks for analyzing the entire transition process by investigating the historical cases of transitions. A multi-phase approach to transition posits this process begins with a pre-development phase characterized by technological and institutional lock-ins, and resistance from incumbent actors. This period is critical for a forward-looking approach to transitions, since early developments shape path-dependent and irreversible processes leading to the emergence of new transition pathways. However, our understanding about the mechanisms and dynamics of this phase is still very limited. This is mainly due to lack of data, weak conceptualization and the necessity of developing new methods proper to deal with these limitations. This dissertation develops methodologies for investigating some complex questions arising in the pre-development phase, by focusing on the case of smart grid development. The first essay uses insights from modeling interventions in complex systems and builds a System Dynamics model to investigate the cost allocation problem of smart metering roll-out. The second essay takes ideas from Technological Innovation System approach and develops a method to analyze the emergence of spatial diversity in smart grid development by combining Social Network Analysis and Agent-Based Modeling. The third essay builds on ideas from network theory and evolutionary modeling to develop a method for identifying the main path of knowledge development and analyzing knowledge trajectories in smart grid initiatives.

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Mohammad Amin Dehdarian
EPFL, 2017
EPFL thesis

Abstract

Official source

https://infoscience.epfl.ch/record/230252?ln=en

About this result

Related concepts (24)

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Related publications (6)

Related publications

Technological innovation strategy in natural resource based industries

Bettina Lynda Bastian

This thesis analyses innovation and technology strategy within a primary industry, notably the upstream oil and gas industry. Industries that exploit and produce finite natural resources are challenged by specific external risks that do not receive much attention from management literature otherwise, especially with regard to organizational innovation behaviour, and thus merit such research focus. On one hand, primary sector firms are confronted with exhaustible nature of finite natural resources. On the other hand, those finite natural resources are fixed to certain locations and companies have to operate in numerous countries in order to exploit resource deposits. In this context, they are often confronted with tremendous socio-political constraints that may have a negative impact on their businesses. This research work raises questions specific to natural resource based industries, notably (1) how can firms defend or even expand their competitive position when they are challenged by the depletion of their core resources? How can firms compensate for an exhaustible resource? (2) How do we evaluate contingencies like political risk for competitive advantage and what role does innovation play in this context? This dissertation takes the format of a "compilation thesis", in this case, three compiled articles. Within the first paper we focus on the resource based view (RBV), a central theoretical approach in management science, which explains how unique firm resources and capabilities contribute to value creation and lead to sustained competitive advantage. We1 apply the assumptions of RBV to the context of finite natural resources and ask if its analytical power is sufficient to explain competitive advantage of companies active in the primary sector, such as hydrocarbon and ore production. Paper 2 analyses how firms respond to geophysical constraints, such as natural resource depletion, and how these depletion issues affect organizational capability building and operational performance in terms of resource exploration and recovery. Paper 3 asks if political risk as external threat influences firm technology and innovation strategies. We find that time horizons of political risk play an important role and analyse why mid-and long term threats positively influence the development of technology and innovation strategies. The dissertation makes several important contributions. First, it contributes to our understanding of innovation and technology strategy within a natural resource based industry. Second, the idea that finite natural resources with their exhaustible nature are different than the "usual" resources investigated by RBV is novel. Third, the analysis also shows how RBV applies to natural resources extracting industries, and how RBV would gain in explicatory power if it was able to integrate factors like "geophysical constraints" and 'exposure to political risk' into the approach. Fourth, the thesis applies RBV and capability approaches to the specific context of primary industries, which is important as it helps to extend the frameworks' usefulness. Fifth, the current thesis combines political risk theory and research on innovation and technology management, and answers why political risk influences firm strategies, differentiation, and entry into new fields. ______________________________ 1 I am going to use the personal pronoun "we" to refer to first person's actions or opinions in this paper. I do so because I want to acknowledge the contributory effort of all who provided ideas and support. Nevertheless, any errors and omissions are completely my own responsibility.

EPFL2009

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The last decade has witnessed a fundamental change in the role of network theory. Side by side with the increasing relevance of interdisciplinarity, this ensemble of mathematical tools and methods has known such a success and such a wide range of different applications to transform it in the proper scientific field of complex networks that groups researchers from different communities. Two different approaches can be distinguished. The first one is descriptive, tightly related to the experimental world. Reality is permeated with complex systems that lend themselves naturally to a network representation. The amount of data on natural, social and artificial systems is constantly increasing, and network theory provides a powerful framework for the analysis of such large datasets. The second is theoretical, aimed at producing models and analytical solutions for the reproduction and understanding of real networks properties. The results presented in this Thesis reflect the interdisciplinarity of the field of complex networks as well as its twofold nature. The first part of the manuscript describes new methods for the study of gene expression and protein interactions. Biological systems are highly complex and sensitive to the environment. Experimental data are affected by errors and poor reproducibility. We propose a method for the comparative analysis of similar datasets. We present an application to three independent studies on gene expression in the cell cycle of the fission yeast that show very poor agreement. In an attempt to reconcile them, we define a periodic genes network in which each node represents a gene identified as periodically regulated in the cell cycle and an edge between two nodes is drawn according to the phase difference between the expression peaks of the corresponding genes. The analysis of the topological structure of the network reveals a universal picture that overcomes the discrepancies and that is strongly related to the biological structure of the cell cycle and to the regulation of its progression. The method is able to group genes that are co-expressed during the cell cycle and to identify putative regulators of the transition from one phase to the next. Proteins are the fundamental product of gene expression. They are characterized by a modular structure comprising independent domains that act as mediators of protein interactions and are common to evolutionary related proteins. We propose the study of domain-centered interaction networks as a complementary tool for the understanding of protein interactions. We explore their topological structure looking for stable similarity groups and we investigate domain-peptide correlations that could be used as patterns for the prediction of the properties and functions to unknown proteins. The second part of this Thesis presents an exact theoretical solution for the navigability of small world networks with an inverse power-law distribution of the length of the connections. Optimal navigability is crucial for real networks involving transportation or communication processes (Internet, electric power grid, neural networks). Most of these systems are small worlds, characterized by a surprisingly small distance between any two nodes that grows only logarithmically with the size. How they are able to exploit this property for optimized performances without a complete knowledge of the structure is still an open question. It has been shown that a small world can arise from a proper compromise between local and long-range connections. In the specific case in which the length of the connections is power-law distributed and a greedy algorithm is used to forward a message from a source to a target, we obtained an original exact solution for the behavior of the expected delivery time in any dimension. We discussed the resulting scenario for the navigability and the implication of different structural parameters.

EPFL2009

This thesis proposes three studies that provide novel empirical evidence on how different types of proximity can affect innovation and science activities through various mechanisms and in different contexts. In the first study (second chapter of this thesis), in collaboration with Julio Raffo, we analyze the relationship between geography and the likelihood of duplication in inventive activities. We argue that the uneven diffusion of knowledge means that the duplication of inventions will not be randomly distributed geographically and over time. First, as knowledge diffuses over time and competitive incentives decrease, the probability of a claimed invention duplicating an existing one will decrease in the time distance between the two. Second, for recent and upcoming inventions, competitive incentives are high, and localized knowledge flows increase the probability of duplication. Therefore, over a brief time period, the probability of duplication decreases with geographic distance. Conversely, the duplication of less recent inventions is more likely to occur at long distances as a consequence of less awareness of a technology existing due to missing knowledge flows. We test our hypotheses on European Patent Office (EPO) patent bibliographical data on patent citation categories. Geographic distance matters significantly less in sectors in which patents are known to be more effective as a source of information such as discrete technologies. In the second study (third chapter), in collaboration with Annamaria Conti and Fabiana Visentin, we investigate the effects of professorsâ social proximity with external universities on the level of productivity of PhD students hired from these universities. Researchers hired from external environments tend to have high scientific productivity compared to those who completed their studies in the same institution where they are employed. In a population of 4,666 PhD students, we further study the scientific productivity of external students from professorsâ networks, defined as students with a master's degree from a different university from that of their PhD, and also from a university with which their supervisors' co-authors are affiliated. We find that these students are significantly more productive, both compared to other students with a master's degree from a different university, and to students with a master's degree from the same university as that of their PhD. In our analyses, we control for the heterogeneity of supervisors and the heterogeneity of institutions where the students obtained their master's degrees, including proxies for the specific relevance of these universities for a given supervisor. Thus, we conclude that professors hire students with higher scientific productivity from universities where their co-authors are affiliated. Additional analyses further suggest that the reduction of information asymmetries is the main mechanism to explain this finding. In the third study (fourth chapter), in collaboration with Guillaume Burghouwt, we investigate the role of interregional knowledge integration as a driver of firm innovative performance. We adopt an unbalanced panel of 3,871 innovative companies in Germany between 1992 and 2010, for a total of 15,819 observations, and we study their innovative productivity. [...]

EPFL2015

Technological innovation strategy in natural resource based industries

Bettina Lynda Bastian

EPFL2009

EPFL2015

Complex networks

Cécile Caretta Cartozo

EPFL2009