Intrastate Crowdfunding in Alaska: Is There Security in Following the Crowd?

This Note analyzes the potential of crowdfunding for the State of Alaska. Crowdfunding can open up new sources of revenue for small businesses while simultaneously providing an avenue for Alaskans to invest in their own communities. The potential, however, must be weighed against the risk of fraud, poorly run businesses, and the lack of protection for investors. It is the responsibility of the Alaska legislature, the State’s securities administrators, and the Securities and Exchange Commission to ensure that investors are adequately protected. This Note discusses Alaska’s crowdfunding legislation, the Alaska Intrastate Crowdfunding Exemption, and recommends changes to the legislation that account for the risks involved in crowdfunding while still capturing its potential

Crowdsourcing Without a Crowd: Reliable Online Species Identification Using Bayesian Models to Minimize Crowd Size

We present an incremental Bayesian model that resolves key issues of crowd size and data quality for consensus labeling. We evaluate our method using data collected from a real-world citizen science program, BeeWatch, which invites members of the public in the United Kingdom to classify (label) photographs of bumblebees as one of 22 possible species. The biological recording domain poses two key and hitherto unaddressed challenges for consensus models of crowdsourcing: (1) the large number of potential species makes classification difficult, and (2) this is compounded by limited crowd availability, stemming from both the inherent difficulty of the task and the lack of relevant skills among the general public. We demonstrate that consensus labels can be reliably found in such circumstances with very small crowd sizes of around three to five users (i.e., through group sourcing). Our incremental Bayesian model, which minimizes crowd size by re-evaluating the quality of the consensus label following each species identification solicited from the crowd, is competitive with a Bayesian approach that uses a larger but fixed crowd size and outperforms majority voting. These results have important ecological applicability: biological recording programs such as BeeWatch can sustain themselves when resources such as taxonomic experts to confirm identifications by photo submitters are scarce (as is typically the case), and feedback can be provided to submitters in a timely fashion. More generally, our model provides benefits to any crowdsourced consensus labeling task where there is a cost (financial or otherwise) associated with soliciting a label

Crowd Motion Capture

International audienceIn this paper a new and original technique to animate a crowd of human beings is presented. Following the success of data-driven animation models (such as motion capture) in the context of articulated figures control, we propose to derivate a similar type of approach for crowd motions. In our framework, the motion of the crowds are represented as a time series of velocity fields estimated from a video of a real crowd. This time series is used as an input of a simple animation model that ”advect” people along this timevarying flow. We demonstrate the power of our technique on both synthetic and real examples of crowd videos. We also introduce the notions of crowd motion editing and present possible extensions to our work

An investigation into the “beautification” of security ceremonies

“Beautiful Security” is a paradigm that requires security ceremonies to contribute to the ‘beauty’ of a user experience. The underlying assumption is that people are likely to be willing to engage with more beautiful security ceremonies. It is hoped that such ceremonies will minimise human deviations from the prescribed interaction, and that security will be improved as a consequence. In this paper, we explain how we went about deriving beautification principles, and how we tested the efficacy of these by applying them to specific security ceremonies. As a first step, we deployed a crowd-sourced platform, using both explicit and metaphorical questions, to extract general aspects associated with the perception of the beauty of real-world security mechanisms. This resulted in the identification of four beautification design guidelines. We used these to beautify the following existing security ceremonies: Italian voting, user-to-laptop authentication, password setup and EU premises access. To test the efficacy of our guidelines, we again leveraged crowd-sourcing to determine whether our “beautified” ceremonies were indeed perceived to be more beautiful than the original ones. The results of this initial foray into the beautification of security ceremonies delivered promising results, but must be interpreted carefully

Determinants Of Demand For Different Types Of Investment Goods

This paper compares the demand for the three individual components of aggregate investment demand: (1) demand by businesses for plant and equipment, (2) business inventory investment and (3) residential housing construction. The models tested are largely based on Keynesian theories of business investment demand, with some allowance for residential housing demand being more driven by Keynes’ consumer demand variables. Other possible determinants of investment are also tested, including ”crowd out” effects of government deficits on business investment and demographic effects on the residential construction market. Annual data for the U.S., 1960 – 2000, are tested using two stage least squares regression techniques modified to eliminate heteroskedasticity in the data. The models are estimated in “first differences”, rather than levels of the data to reduce the effects of multicollinearity, non stationarity and autocorrelation. The models explain about 90% of the variance in plant and equipment demand, 85% of the variance in residential housing demand for and 67% of inventory demand. The results indicate that demand for each of these three types of investment goods is driven by different combinations of variables Business investment in plant and equipment appears determined by how much the overall economy is growing (the accelerator effect), the availability of credit (crowd out), the availability of depreciation reserves, the prime interest rate lagged three years, business profits and stock values lagged one year, and the effects of an exchange rate change over the four year period following the change. Inventory investment seems mainly determined by availability of depreciation reserves, crowd out, interest rates, unexpected changes in consumer demand and the accelerator. Residential construction demand seems mainly driven by disposable income, the effect of general growth in the economy on consumer spending (the accelerator), credit availability (crowd out), current year mortgage rates, and prior year consumer wealth levels.

Following the Crowd: Beginners Investors Guide to the Options Market

While the options market may be intimidating for a beginner, having the right tools can help improve the outcome of their investments. This project aims to develop a tool that uses time-series analysis and forecasting to model the future demand of S&P 500 and AAPL options contracts. The open interest of these contracts will be analyzed using various models such as AR, ARIMA, Neural Networks, and VAR, along with the put-call ratio. The goal is not to make buy or sell recommendations, but alert the user when money is flowing into a security or index. Of all the models, the use of the ARMA model provides the best results for predicting the open interest in contracts for these specific symbols

Are Subjective Evaluations Biased by Social Factors or Connections? An Econometric Analysis of Soccer Referee Decisions

Many incentive contracts are based on subjective evaluations and contractual disputes depend on judges’ decisions. However, subjective evaluations raise risks of favouritism and distortions. Sport contests are a fruitful field for testing empirically theories of incentives. In this paper the behaviour of the referees in the Italian soccer (football) league (“Serie A”) is analyzed. Using data on injury (or extra) time subjectively assigned by the referee at the end of the match and controlling for factors which may influence it (players substitutions, yellow and red cards, penalty kicks, etc.), we show that referees are biased in favour of home team, in that injury time is significantly greater if home teams are losing. The refereeing bias increases greatly when there is no running track in the stadium and the crowd is close to the pitch. Following the 2006 “Serie A” scandal we test whether favouritism emerges towards teams suspected of connections with referees finding that these teams obtain favourable decisions. Social pressure by the crowd attending the match however appears to be the main cause of favouritism.Favoritism, Subjective evaluation; Sport economics; Soccer; Referee bias;