ERC FUNDED PROJECTS

I intend to investigate what cognitive mechanisms give us combinatorial speech. Combinatorial speech is the ability to make new words using pre-existing speech sounds. Humans are the only apes that can do this, yet we do not know how our brains do it, nor how exactly we differ from other apes. Using new experimental techniques to study human behavior and new computational techniques to model human cognition, I will find out how we deal with combinatorial speech. The experimental part will study individual and cultural learning. Experimental cultural learning is a new technique that simulates cultural evolution in the laboratory. Two types of cultural learning will be used: iterated learning, which simulates language transfer across generations, and social coordination, which simulates emergence of norms in a language community. Using the two types of cultural learning together with individual learning experiments will help to zero in, from three angles, on how humans deal with combinatorial speech. In addition it will make a methodological contribution by comparing the strengths and weaknesses of the three methods. The computer modeling part will formalize hypotheses about how our brains deal with combinatorial speech. Two models will be built: a high-level model that will establish the basic algorithms with which combinatorial speech is learned and reproduced, and a neural model that will establish in more detail how the algorithms are implemented in the brain. In addition, the models, through increasing understanding of how humans deal with speech, will help bridge the performance gap between human and computer speech recognition. The project will advance science in four ways: it will provide insight into how our unique ability for using combinatorial speech works, it will tell us how this is implemented in the brain, it will extend the novel methodology of experimental cultural learning and it will create new computer models for dealing with human speech.

1 276 620 €

Start date: 2012-02-01, End date: 2017-01-31

Rates of domestic violence and the relative risk of premature death for women are higher in sub-Saharan Africa than in any other region. Yet we know remarkably little about the economic forces, incentives and constraints that drive discrimination against women in this region, making it hard to identify policy levers to address the problem. This project will help fill this gap. I will investigate gender discrimination from two complementary perspectives. First, through the lens of economic history, I will investigate the forces driving trends in women’s relative well-being since slavery. To quantify the evolution of well-being of sub-Saharan women relative to men, I will use three types of historical data: anthropometric indicators (relative height), vital statistics (to compute numbers of missing women), and outcomes of formal and informal family law disputes. I will then investigate how major economic developments and changes in family laws differentially affected women’s welfare across ethnic groups with different norms on women’s roles and rights. Second, using intra-household economic models, I will provide new insights into domestic violence and gender bias in access to crucial resources in present-day Africa. I will develop a new household model that incorporates gender identity and endogenous outside options to explore the relationship between women’s empowerment and the use of violence. Using the notion of strategic delegation, I will propose a new rationale for the separation of budgets often observed in African households and generate predictions of how improvements in women’s outside options affect welfare. Finally, with first hand data, I will investigate intra-household differences in nutrition and work effort in times of food shortage from the points of view of efficiency and equity. I will use activity trackers as an innovative means of collecting high quality data on work effort and thus overcome data limitations restricting the existing literature

1 499 313 €

Start date: 2018-08-01, End date: 2023-07-31

Present-day financial markets are turning algorithmic, as market orders are increasingly being executed by fully automated computer algorithms, without any direct human intervention. Although algorithmic finance seems to fundamentally reshape the central dynamics in financial markets, and even though it prompts core sociological questions, it has not yet received any systematic attention. In a pioneering contribution to economic sociology and social studies of finance, ALGOFINANCE aims to understand how and with what consequences the turn to algorithms is changing financial markets. The overall concept and central contributions of ALGOFINANCE are the following: (1) on an intra-firm level, the project examines how the shift to algorithmic finance reshapes the ways in which trading firms operate, and does so by systematically and empirically investigating the reconfiguration of organizational structures and employee subjectivity; (2) on an inter-algorithmic level, it offers a ground-breaking methodology (agent-based modelling informed by qualitative data) to grasp how trading algorithms interact with one another in a fully digital space; and (3) on the level of market sociality, it proposes a novel theorization of how intra-firm and inter-algorithmic dynamics can be conceived of as introducing a particular form of sociality that is characteristic to algorithmic finance: a form of sociality-as-association heuristically analyzed as imitation. None of these three levels have received systematic attention in the state-of-the-art literature. Addressing them will significantly advance the understanding of present-day algorithmic finance in economic sociology. By contributing novel empirical, methodological, and theoretical understandings of the functioning and consequences of algorithms, ALGOFINANCE will pave the way for other research into digital sociology and the broader algorithmization of society.

1 590 036 €

Start date: 2017-05-01, End date: 2021-04-30

Wireless power transfer (WPT), pioneered by Tesla, is an idea at least as old as radio communications. However, on the one hand, due to health concerns and the large antenna dimensions required for transmission of high energy levels, until recently WPT has been limited mostly to very short distance applications. On the other hand, recent advances in silicon technology have significantly reduced the energy needs of electronic systems, making WPT over radio waves a potential source of energy for low power devices. Although WPT through radio waves has already found various short-range applications (such as the radio-frequency identification technology, healthcare monitoring etc.), its integration as a building block in the operation of wireless communications systems is still unexploited. On the other hand, conventional radio wave based information and energy transmissions have largely been designed separately. However, many applications can benefit from simultaneous wireless information and power transfer (SWIPT). The overall objective of the APOLLO project is to study the integration of WPT/SWIPT technology into future wireless communication systems. Compared to past and current research efforts in this area, our technical approach is deeply interdisciplinary and more comprehensive, combining the expertise of wireless communications, control theory, information theory, optimization, and electronics/microwave engineering. The key outcomes of the project include: 1) a rigorous and complete mathematical theory for WPT/SWIPT via information/communication/control theoretic studies; 2) new physical and cross-layer mechanisms that will enable the integration of WPT/SWIPT into future communication systems; 3) new network architectures that will fully exploit potential benefits of WPT/SWIPT; and 4) development of a proof-of-concept by implementing highly-efficient and multi-band metamaterial energy harvesting sensors for SWIPT.

1 930 625 €

Start date: 2019-07-01, End date: 2024-06-30