Project acronym BIGlobal
Project Firm Growth and Market Power in the Global Economy
Researcher (PI) Swati DHINGRA
Host Institution (HI) LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE
Call Details Starting Grant (StG), SH1, ERC-2017-STG
Summary According to the European Commission, to design effective policies for ensuring a “more dynamic, innovative and competitive” economy, it is essential to understand the decision-making process of firms as they differ a lot in terms of their capacities and policy responses (EC 2007). The objective of my future research is to provide such an analysis. BIGlobal will examine the sources of firm growth and market power to provide new insights into welfare and policy in a globalized world.
Much of analysis of the global economy is set in the paradigm of markets that allocate resources efficiently and there is little role for policy. But big firms dominate economic activity, especially across borders. How do firms grow and what is the effect of their market power on the welfare impact of globalization? This project will determine how firm decisions matter for the aggregate gains from globalization, the division of these gains across different individuals and their implications for policy design.
Over the next five years, I will incorporate richer firms behaviour in models of international trade to understand how trade and industrial policies impact the growth process, especially in less developed markets. The specific questions I will address include: how can trade and competition policy ensure consumers benefit from globalization when firms engaged in international trade have market power, how do domestic policies to encourage agribusiness firms affect the extent to which small farmers gain from trade, how do industrial policies affect firm growth through input linkages, and what is the impact of banking globalization on the growth of firms in the real sector.
Each project will combine theoretical work with rich data from developing economies to expand the frontier of knowledge on trade and industrial policy, and to provide a basis for informed policymaking.
Summary
According to the European Commission, to design effective policies for ensuring a “more dynamic, innovative and competitive” economy, it is essential to understand the decision-making process of firms as they differ a lot in terms of their capacities and policy responses (EC 2007). The objective of my future research is to provide such an analysis. BIGlobal will examine the sources of firm growth and market power to provide new insights into welfare and policy in a globalized world.
Much of analysis of the global economy is set in the paradigm of markets that allocate resources efficiently and there is little role for policy. But big firms dominate economic activity, especially across borders. How do firms grow and what is the effect of their market power on the welfare impact of globalization? This project will determine how firm decisions matter for the aggregate gains from globalization, the division of these gains across different individuals and their implications for policy design.
Over the next five years, I will incorporate richer firms behaviour in models of international trade to understand how trade and industrial policies impact the growth process, especially in less developed markets. The specific questions I will address include: how can trade and competition policy ensure consumers benefit from globalization when firms engaged in international trade have market power, how do domestic policies to encourage agribusiness firms affect the extent to which small farmers gain from trade, how do industrial policies affect firm growth through input linkages, and what is the impact of banking globalization on the growth of firms in the real sector.
Each project will combine theoretical work with rich data from developing economies to expand the frontier of knowledge on trade and industrial policy, and to provide a basis for informed policymaking.
Max ERC Funding
1 313 103 €
Duration
Start date: 2017-12-01, End date: 2022-11-30
Project acronym EMBED
Project Embedded Markets and the Economy
Researcher (PI) Matthew ELLIOTT
Host Institution (HI) THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Call Details Starting Grant (StG), SH1, ERC-2017-STG
Summary EMBED takes a microeconomic approach to investigating the macroeconomic implications of market transactions being embedded in social relationships. Sociologists and economists have documented the importance of relationships for mediating trade in a wide range of market settings. EMBED seeks to investigate the implications of this for the economy as a whole.
Ethnographic work suggests that relationships foster common understandings which limit opportunistic behaviour. Subproject 1 will develop a first relational contacting theory of networked markets to capture this, and test these predictions using data from the Bundesbank. Formally modelling dynamic business-relationships, these relationships can be viewed as social capital. We will investigate whether this social capital is destroyed by economic shocks, and if so how long it takes to rebuild.
Subproject 2 will run a field experiment. We will intervene in a networked market to create new relationships in a variety of ways. The varying success of these approaches will help us better understand the role of relationships in markets. Moreover, as a result we’ll get exogenous variation in the market structure that will help identity the affects relationships have on market outcomes.
Subproject 3 will explore frictions in the clearing of networked markets. As the data requirements to empirically test between different theories are extremely demanding, laboratory experiments will be run. Breaking convention, these experiments will be protocol-free, although interactions will be closely monitored. This will create a more level playing field for testing different theories while also creating scope for the market to develop efficiency enhancing norms.
Subproject 4 will examine firm level multi-sourcing and production technology decisions, and how these feed into the creation of supply chains. The fragility of these supply chains will be investigated and equilibrium supply chains compared across countries.
Summary
EMBED takes a microeconomic approach to investigating the macroeconomic implications of market transactions being embedded in social relationships. Sociologists and economists have documented the importance of relationships for mediating trade in a wide range of market settings. EMBED seeks to investigate the implications of this for the economy as a whole.
Ethnographic work suggests that relationships foster common understandings which limit opportunistic behaviour. Subproject 1 will develop a first relational contacting theory of networked markets to capture this, and test these predictions using data from the Bundesbank. Formally modelling dynamic business-relationships, these relationships can be viewed as social capital. We will investigate whether this social capital is destroyed by economic shocks, and if so how long it takes to rebuild.
Subproject 2 will run a field experiment. We will intervene in a networked market to create new relationships in a variety of ways. The varying success of these approaches will help us better understand the role of relationships in markets. Moreover, as a result we’ll get exogenous variation in the market structure that will help identity the affects relationships have on market outcomes.
Subproject 3 will explore frictions in the clearing of networked markets. As the data requirements to empirically test between different theories are extremely demanding, laboratory experiments will be run. Breaking convention, these experiments will be protocol-free, although interactions will be closely monitored. This will create a more level playing field for testing different theories while also creating scope for the market to develop efficiency enhancing norms.
Subproject 4 will examine firm level multi-sourcing and production technology decisions, and how these feed into the creation of supply chains. The fragility of these supply chains will be investigated and equilibrium supply chains compared across countries.
Max ERC Funding
1 449 106 €
Duration
Start date: 2018-06-01, End date: 2023-05-31
Project acronym Real-PIM-System
Project Memristive In-Memory Processing System
Researcher (PI) shahar KVATINSKY
Host Institution (HI) TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
Call Details Starting Grant (StG), PE7, ERC-2017-STG
Summary Our project aims to develop a new computer architecture that enables true in-memory processing based on a unit that can both store and process data using the same cells. This unit, called a memristive memory processing unit (mMPU), will substantially reduce the necessity to move data in computing systems, solving the two main bottlenecks exist in current computing systems, i.e., speed ('memory wall') and energy efficiency ('power wall'). Emerging memory technologies, namely memristive devices, are the enablers of the mMPU. While memristors are naturally used as memory, these novel devices can also perform logical operations using a technique we have invented called Memristor Aided Logic (MAGIC). This combination is the basis of mMPU.
The goal of this research is to design a fully functional mMPU, and by that, to demonstrate a real computing system with significantly improved performance and energy efficiency. We have identified four main research tasks which must be completed to demonstrate a full system utilizing mMPU: mMPU design, system architecture and software, modeling and evaluation, and fabrication. Both memristive memory array and mMPU control will be designed and optimized for different technologies in the first objective. The second objective will deal with the different aspects of the system, including programming model, different mMPU modes of operation and their corresponding system implications, compiler and operating systems. For system evaluation, we will develop models and tools in the third objective in order to measure the performance, area and energy and to compare them to other state-of-the-art computing systems. Lastly, we will fabricate the different parts of the system to demonstrate the full system.
Encouraged from our preliminary experimental results, we expect to achieve 10X improvement in performance, and 100X improvement in energy efficiency as compared to state-of-the-art von Neumann systems when working with appropriate workloads.
Summary
Our project aims to develop a new computer architecture that enables true in-memory processing based on a unit that can both store and process data using the same cells. This unit, called a memristive memory processing unit (mMPU), will substantially reduce the necessity to move data in computing systems, solving the two main bottlenecks exist in current computing systems, i.e., speed ('memory wall') and energy efficiency ('power wall'). Emerging memory technologies, namely memristive devices, are the enablers of the mMPU. While memristors are naturally used as memory, these novel devices can also perform logical operations using a technique we have invented called Memristor Aided Logic (MAGIC). This combination is the basis of mMPU.
The goal of this research is to design a fully functional mMPU, and by that, to demonstrate a real computing system with significantly improved performance and energy efficiency. We have identified four main research tasks which must be completed to demonstrate a full system utilizing mMPU: mMPU design, system architecture and software, modeling and evaluation, and fabrication. Both memristive memory array and mMPU control will be designed and optimized for different technologies in the first objective. The second objective will deal with the different aspects of the system, including programming model, different mMPU modes of operation and their corresponding system implications, compiler and operating systems. For system evaluation, we will develop models and tools in the third objective in order to measure the performance, area and energy and to compare them to other state-of-the-art computing systems. Lastly, we will fabricate the different parts of the system to demonstrate the full system.
Encouraged from our preliminary experimental results, we expect to achieve 10X improvement in performance, and 100X improvement in energy efficiency as compared to state-of-the-art von Neumann systems when working with appropriate workloads.
Max ERC Funding
1 500 000 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym SBS3-5
Project Stimulated Brillouin Scattering based RF to Optical Signal Transduction and Amplification
Researcher (PI) Krishna COIMBATORE BALRAM
Host Institution (HI) UNIVERSITY OF BRISTOL
Call Details Starting Grant (StG), PE7, ERC-2017-STG
Summary While the detection of weak signals (down to the single photon level) in the optical frequency range is routine on account of the high photon energy (compared to thermal excitation energy kBT) and the availability of efficient detectors, this is not the case in the radio frequency (RF) and microwave frequency regimes wherein thermal (Johnson) noise in detectors swamps out the faint RF signals (in applications from radio astronomy, MRI to radar) and requires the use of cryogenic amplifiers. The ability to map signals efficiently from the microwave to optical regime becomes paramount for distant systems to communicate with each other using low loss telecom fibers. Both classical (radio over fiber systems) and quantum (linking two superconducting qubit processors in two dilution fridges) information processing systems will benefit greatly from the development of an efficient RF to optical signal transducer.
I have been developing efficient RF to optical transduction schemes in GaAs cavity optomechanical systems (KC Balram et al., Nature Photonics (2016)) by exploiting its favorable piezoelectric (for coupling RF signals to propagating acoustic waves) and elasto-optic (for engineering strong acousto-optic interactions) properties. In this project, I would like to extend this work and address the issue of weak RF signal detection by up-converting RF signals to the optical domain using integrated Stimulated Brillouin Scattering (SBS) and shot-noise limited optical detection. Piezoelectric SBS systems can also be used to build high frequency, high gain RF amplifiers with noise figures that can be lower than conventional RF amplifiers. Working in a novel GaAs on insulator platform helps provide some unique advantages (tightly confined acoustic and optical modes with large modal overlap and a large elasto-optic coefficient leading to significant Brillouin gain) while holding the potential for interfacing complex circuitry in a well-established III-V materials platform.
Summary
While the detection of weak signals (down to the single photon level) in the optical frequency range is routine on account of the high photon energy (compared to thermal excitation energy kBT) and the availability of efficient detectors, this is not the case in the radio frequency (RF) and microwave frequency regimes wherein thermal (Johnson) noise in detectors swamps out the faint RF signals (in applications from radio astronomy, MRI to radar) and requires the use of cryogenic amplifiers. The ability to map signals efficiently from the microwave to optical regime becomes paramount for distant systems to communicate with each other using low loss telecom fibers. Both classical (radio over fiber systems) and quantum (linking two superconducting qubit processors in two dilution fridges) information processing systems will benefit greatly from the development of an efficient RF to optical signal transducer.
I have been developing efficient RF to optical transduction schemes in GaAs cavity optomechanical systems (KC Balram et al., Nature Photonics (2016)) by exploiting its favorable piezoelectric (for coupling RF signals to propagating acoustic waves) and elasto-optic (for engineering strong acousto-optic interactions) properties. In this project, I would like to extend this work and address the issue of weak RF signal detection by up-converting RF signals to the optical domain using integrated Stimulated Brillouin Scattering (SBS) and shot-noise limited optical detection. Piezoelectric SBS systems can also be used to build high frequency, high gain RF amplifiers with noise figures that can be lower than conventional RF amplifiers. Working in a novel GaAs on insulator platform helps provide some unique advantages (tightly confined acoustic and optical modes with large modal overlap and a large elasto-optic coefficient leading to significant Brillouin gain) while holding the potential for interfacing complex circuitry in a well-established III-V materials platform.
Max ERC Funding
1 712 581 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym SPADE
Project from SPArsity to DEep learning
Researcher (PI) Raja Giryes
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Starting Grant (StG), PE7, ERC-2017-STG
Summary Lately, deep learning (DL) has become one of the most powerful machine learning tools with ground-breaking results in computer vision, signal & image processing, language processing, and many other domains. However, one of its main deficiencies is the lack of theoretical foundation. While some theory has been developed, it is widely agreed that DL is not well-understood yet.
A proper understanding of the learning mechanism and architecture is very likely to broaden the great success to new fields and applications. In particular, it has the promise of improving DL performance in the unsupervised regime and on regression tasks, where it is currently lagging behind its otherwise spectacular success demonstrated in massively-supervised classification problems.
A somewhat related and popular data model is based on sparse-representations. It led to cutting-edge methods in various fields such as medical imaging, computer vision and signal & image processing. Its success can be largely attributed to its well-established theoretical foundation, which boosted the development of its various ramifications. Recent work suggests a close relationship between this model and DL, although this bridge is not fully clear nor developed.
This project revolves around the use of sparsity with DL. It aims at bridging the fundamental gap in the theory of DL using tools applied in sparsity, highlighting the role of structure in data as the foundation for elucidating the success of DL. It also aims at using efficient DL methods to improve the solution of problems using sparse models. Moreover, this project pursues a unified theoretical framework merging sparsity with DL, in particular migrating powerful unsupervised learning concepts from the realm of sparsity to that of DL. A successful marriage between the two fields has a great potential impact of giving rise to a new generation of learning methods and architectures and bringing DL to unprecedented new summits in novel domains and tasks.
Summary
Lately, deep learning (DL) has become one of the most powerful machine learning tools with ground-breaking results in computer vision, signal & image processing, language processing, and many other domains. However, one of its main deficiencies is the lack of theoretical foundation. While some theory has been developed, it is widely agreed that DL is not well-understood yet.
A proper understanding of the learning mechanism and architecture is very likely to broaden the great success to new fields and applications. In particular, it has the promise of improving DL performance in the unsupervised regime and on regression tasks, where it is currently lagging behind its otherwise spectacular success demonstrated in massively-supervised classification problems.
A somewhat related and popular data model is based on sparse-representations. It led to cutting-edge methods in various fields such as medical imaging, computer vision and signal & image processing. Its success can be largely attributed to its well-established theoretical foundation, which boosted the development of its various ramifications. Recent work suggests a close relationship between this model and DL, although this bridge is not fully clear nor developed.
This project revolves around the use of sparsity with DL. It aims at bridging the fundamental gap in the theory of DL using tools applied in sparsity, highlighting the role of structure in data as the foundation for elucidating the success of DL. It also aims at using efficient DL methods to improve the solution of problems using sparse models. Moreover, this project pursues a unified theoretical framework merging sparsity with DL, in particular migrating powerful unsupervised learning concepts from the realm of sparsity to that of DL. A successful marriage between the two fields has a great potential impact of giving rise to a new generation of learning methods and architectures and bringing DL to unprecedented new summits in novel domains and tasks.
Max ERC Funding
1 499 375 €
Duration
Start date: 2017-10-01, End date: 2022-09-30
Project acronym TheSocialBusiness
Project The advantages and pitfalls of elicitated online user engagement
Researcher (PI) Gal Oestreicher-Singer
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Starting Grant (StG), SH1, ERC-2017-STG
Summary The notion that websites benefit when their users are socially engaged—i.e., when they interact with content and with other users—has become so entrenched it is practically an axiom. Accordingly, websites in numerous domains invest heavily in ‘social computing’ features that encourage such engagement. In fact, many attempt to elicit engagement proactively, through the use of calls to action—prompts that ask users to carry out participatory actions such as rating or ‘liking’ content. Given the vast popularity of social computing, it is surprising how little we actually know about how user engagement affects websites and their users. From a business perspective, the direct value of user engagement is far from clear. From a societal perspective, it is unclear whether the increasing expectation for users to engage with firms may lead users to behave in ways that do not serve them. This research aims to provide a comprehensive understanding of user engagement, and specifically, engagement elicited by calls to action, from those two perspectives. I will use an empirical approach, relying on innovative lab and large-scale field experiments. The lab experiments leverage a specially-designed website. For the field experiments, we will collaborate with websites spanning several domains; we have already initiated a relationship with a leading website-development service provider that uses a freemium business model, and have been able to observe the actual behavior of its users. Our preliminary results are promising, supporting the idea that calls to action have strong effects on conversion and information revelation. Moving forward, I plan to fully characterize the nature of these effects in multiple product domains, and to isolate their underlying mechanisms. I am confident that this research program will transform our understanding of the economic and broader societal impact of the social computing phenomenon.
Summary
The notion that websites benefit when their users are socially engaged—i.e., when they interact with content and with other users—has become so entrenched it is practically an axiom. Accordingly, websites in numerous domains invest heavily in ‘social computing’ features that encourage such engagement. In fact, many attempt to elicit engagement proactively, through the use of calls to action—prompts that ask users to carry out participatory actions such as rating or ‘liking’ content. Given the vast popularity of social computing, it is surprising how little we actually know about how user engagement affects websites and their users. From a business perspective, the direct value of user engagement is far from clear. From a societal perspective, it is unclear whether the increasing expectation for users to engage with firms may lead users to behave in ways that do not serve them. This research aims to provide a comprehensive understanding of user engagement, and specifically, engagement elicited by calls to action, from those two perspectives. I will use an empirical approach, relying on innovative lab and large-scale field experiments. The lab experiments leverage a specially-designed website. For the field experiments, we will collaborate with websites spanning several domains; we have already initiated a relationship with a leading website-development service provider that uses a freemium business model, and have been able to observe the actual behavior of its users. Our preliminary results are promising, supporting the idea that calls to action have strong effects on conversion and information revelation. Moving forward, I plan to fully characterize the nature of these effects in multiple product domains, and to isolate their underlying mechanisms. I am confident that this research program will transform our understanding of the economic and broader societal impact of the social computing phenomenon.
Max ERC Funding
1 487 500 €
Duration
Start date: 2018-06-01, End date: 2023-05-31
