Enterprise, Innovation and the Enviroment

This report aims to profile the activity of small and medium sized enterprises (SME) operating in the UK's environmental sector. The environmental sector is expected to be critical for the future economic development of the UK supporting a new low carbon economy. As many entrepreneurial and innovative firms require significant funding to support their innovative activities this report specifically investigates the innovation activity and financial requirements UK environmental companies. The survey report highlights the role of a variety of funding sources, including public investment for supporting these firms, particularly those active in R&D. Responding firms show a strong record of performance to date, particularly R&D active companies but also have expectations of additional finance being made available for the future in the form of loans, equity finance and government funding

Survey of the literature on innovation and economic performance

Despite very strong differences in their treatment of technological change in economic theory, both the neoclassical and the more Schumpetarian (and evolutionary) economic approaches often assume that market selection rewards the most innovative firms. However, despite such strong assumptions, empirical evidence on whether innovative firms perform better than non-innovative firms remains inconclusive. If innovators do not grow more, does this imply that market selection fails? And does the different impact of innovation on industrial performance (measured by firm growth and profitability) and financial performance (measured by market value and stock returns) signal differences in how industrial and financial markets react to firm level efforts around innovation? This discussion paper reviews the literature on the interaction between innovation and economic/financial performance, and outlines the way that work within FINNOV Work Package 2 (SELECTION), Co-Evolution of Industry Dynamics and Financial Dynamics, will contribute to better understanding this interaction

Collective behavior of El Farol attendees

Arthur’s paradigm of the El Farol bar for modeling bounded rationality and inductive behavior is undertaken. The memory horizon available to the agents and the selection criteria they utilize for the prediction algorithm are the two essential variables identified to represent the heterogeneity of agent strategies. The latter is enriched by including various rewarding schemes during decision making. Though the external input of comfort level is not explicitly coded in the algorithm pool, it contributes to each agent’s decision process. Playing with the essential variables, one can maneuver the overall outcome between the comfort level and the endogenously identified limiting state. The distribution of algorithm clusters significantly varies for shorter agent memories. This in turn affects the long-term aggregated dynamics of attendances. We observe that a transition occurs in the attendance distribution at the critical memory horizon where the correlations of the attendance deviations take longer time to decay to zero. A larger part of the crowd becomes more comfortable while the rest of the bar-goers still feel the congestion for long memories. Agents’ confidence on their algorithms and the delayed feedback of attendance data increase the overall collectivity of the system behavior

The Effects of Selective and Indiscriminate Repression on the 2013 Gezi Park Nonviolent Resistance Campaign

We investigate the differential effects of selective and indiscriminate repression on the rate of protest actions during the nonviolent resistance campaign in Gezi Park, Turkey, in 2013. After deriving theoretical expectations about how and why these forms of repression will influence protest actions, we test them with protest event data that were collected from a major local newspaper and subsequently validated through a comparison with two other independent Twitter datasets. Utilizing a Poisson autoregressive estimation model, we find that selective repression, as measured by the number of arrested activists who were detained while they were not demonstrating, decreased the rate of protest actions. Meanwhile, indiscriminate repression, as measured by the frequency of the government’s use of lethal and nonlethal violence against protesters during demonstrations, increased the rate of protest actions. Our findings support prior research on the influence of indiscriminate repression on backfire outcomes. They also provide evidence for the impact of selective repression on movement demobilization through the removal of opposition activists. Finally, the targeted arrest strategy of selective repression that was employed in the Gezi campaign has implications for the feasibility of the strategic incapacitation model of protest policing

Identification of appropriate temporal scales of dominant low flow indicators in the Main River, Germany

Models incorporating the appropriate temporal scales of dominant indicators for low flows are assumed to perform better than models with arbitrary selected temporal scales. In this paper, we investigate appropriate temporal scales of dominant low flow indicators: precipitation (P), evapotranspiration (ET) and the standardized groundwater storage index (G). This analysis is done in the context of low flow forecasting with a lead time of 14 days in the Main River, a tributary of the Rhine River, located in Germany. Correlation coefficients (i.e. Pearson, Kendall and Spearman) are used to reveal the appropriate temporal scales of dominant low flow indicators at different time lags between low flows and indicators and different support scales of indicators. The results are presented for lag values and support scales, which result in correlation coefficients between low flows and dominant indicators falling into the maximum 10% percentile range. P has a maximum Spearman correlation coefficient (ρ) of 0.38 (p = 0.95) at a support scale of 336 days and a lag of zero days. ET has a maximum ρ of –0.60 (p = 0.95) at a support scale of 280 days and a lag of 56 days and G has a maximum ρ of 0.69 (p = 0.95) at a support scale of 7 days and a lag of 3 days. The identified appropriate support scales and lags can be used for low flow forecasting with a lead time of 14 days