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"Social prediction Fiction."
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Wild Jack
Clive Anderson is falsely accused of questioning the status quo and must escape from a twenty-third century \"retraining school.\"
Book
Urban growth simulation in different scenarios using the SLEUTH model: A case study of Hefei, East China
As uncontrolled urban growth has increasingly challenged the sustainable use of urban land, it is critically important to model urban growth from different perspectives. Using the SLEUTH (Slope, Land use, Exclusion, Urban, Transportation, and Hill-shade) model, the historical data of Hefei in 2000, 2005, 2010, and 2015 were collected and input to simulate urban growth from 2015 to 2040. Three different urban growth scenarios were considered, namely a historical growth scenario, an urban planning growth scenario, and a land suitability growth scenario. Prediction results show that by 2040 urban built-up land would increase to 1434 km2 in the historical growth scenario, to 1190 km2 in the urban planning growth scenario, and to 1217 km2 in the land suitability growth scenario. We conclude that (1) exclusion layers without effective limits might result in unreasonable prediction of future built-up land; (2) based on the general land use map, the urban growth prediction took the governmental policies into account and could reveal the development hotspots in urban planning; and (3) the land suitability scenario prediction was the result of the trade-off between ecological land and built-up land as it used the MCR -based (minimum cumulative resistance model) land suitability assessment result. It would help to form a compact urban space and avoid excessive protection of farmland and ecological land. Findings derived from this study may provide urban planners with interesting insights on formulating urban planning strategies.
Journal Article
The pool of fire
Will and a small group of free people plan to destroy the three great cities of the Tripods before the arrival of a space ship destined to doom humanity.
Book
Accurate Machine Learning Predictions of Sci-Fi Film Performance
A groundbreaking method is introduced to leverage machine learning algorithms to revolutionize the prediction of success rates for science fiction films. In the captivating world of the film industry, extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut. Our study aims to harness the power of available data to estimate a film’s early success rate. With the vast resources offered by the internet, we can access a plethora of movie-related information, including actors, directors, critic reviews, user reviews, ratings, writers, budgets, genres, Facebook likes, YouTube views for movie trailers, and Twitter followers. The first few weeks of a film’s release are crucial in determining its fate, and online reviews and film evaluations profoundly impact its opening-week earnings. Hence, our research employs advanced supervised machine learning techniques to predict a film’s triumph. The Internet Movie Database (IMDb) is a comprehensive data repository for nearly all movies. A robust predictive classification approach is developed by employing various machine learning algorithms, such as fine, medium, coarse, cosine, cubic, and weighted KNN. To determine the best model, the performance of each feature was evaluated based on composite metrics. Moreover, the significant influences of social media platforms were recognized including Twitter, Instagram, and Facebook on shaping individuals’ opinions. A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms. The findings of this study demonstrate that the chosen algorithms offer more precise estimations, faster execution times, and higher accuracy rates when compared to previous research. By integrating the features of existing prediction models and social media sentiment analysis models, our proposed approach provides a remarkably accurate prediction of a movie’s success. This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release, allowing them to tailor their promotional activities accordingly. Furthermore, the adopted research lays the foundation for developing even more accurate prediction models, considering the ever-increasing significance of social media platforms in shaping individuals’ opinions. In conclusion, this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films, opening new avenues for the film industry.
Journal Article
The Past, Present, and Future States of Political Theory
Beginning with a historical perspective on the long and short past of political theory, I argue for three priorities for the field’s future: (1) theorizing why and how constitutional democracies corrode and die, and what might be done to stop rising authoritarianism and fascism, as well as racism and misogyny, in liberal egalitarian political systems; (2) the advancement of more predictive and future-oriented forms of political theory to address democratic corruption, democratic backsliding into authoritarianism, and other urgent political problems; and (3) the need to diversify the field and the wider discipline of political science by advancing women and people of color. To stay true to its own history, political theory should lend a helping hand to politics and society when democracy is in crisis.
Journal Article
Platform Policing and the Real-Time Cop
Policing, particularly in the United States, is being progressively datafied. This process has a historical trajectory that is crucial to the analysis and critique of new platform-based security architectures. Predictive policing has already attracted considerable attention, partially due to its seemingly novel fusion of predictive analytics and police work. Hyperbolic early claims—often mobilizing science fiction imagery—that the future could, in fact, be predicted, pointed towards utopic/dystopic imaginaries of seamlessly integrated control. Predictive policing is, however, increasingly only one component of cloud-based data systems that are coursing through police activity. The imaginary of these transformations can be analysed through the security imaginary of policing as a process of real-time data transmission, perpetually self-adjusting and self-augmenting through machine calculation. The historical contextualization of this imaginary suggests useful vectors of inquiry that position platform policing squarely within the mechanisms of contemporary capitalism.
Journal Article
The effect of prediction error on episodic memory encoding is modulated by the outcome of the predictions
Expectations can lead to prediction errors of varying degrees depending on the extent to which the information encountered in the environment conforms with prior knowledge. While there is strong evidence on the computationally specific effects of such prediction errors on learning, relatively less evidence is available regarding their effects on episodic memory. Here, we had participants work on a task in which they learned context/object-category associations of different strengths based on the outcomes of their predictions. We then used a reinforcement learning model to derive subject-specific trial-to-trial estimates of prediction error at encoding and link it to subsequent recognition memory. Results showed that model-derived prediction errors at encoding influenced subsequent memory as a function of the outcome of participants’ predictions (correct vs. incorrect). When participants correctly predicted the object category, stronger prediction errors (as a consequence of weak expectations) led to enhanced memory. In contrast, when participants incorrectly predicted the object category, stronger prediction errors (as a consequence of strong expectations) led to impaired memory. These results highlight the important moderating role of choice outcome that may be related to interactions between the hippocampal and striatal dopaminergic systems.
Journal Article
Handwriting, spelling, and narrative competence in the fictional stories of Italian primary-school children
Previous studies with English-speaking children showed that handwriting and spelling abilities played a critical role in the development of writing fluency and quality, particularly during the transition from the kindergarten to primary school; in contrast, studies dealing with orthographically transparent languages found small or no relations between transcription skills and writing competence. The present study examined the question of whether, in a transparent language like Italian, spelling and handwriting abilities predicted the narrative competence of primary-school children, and whether their effects varied as a function of grade. To this purpose, 150 Italian-speaking children (77 boys and 73 girls), attending the third, fourth, and fifth grades, were examined: they underwent an assessment of receptive vocabulary and wrote a fictional story starting from a keyword. Written compositions were scored for handwriting quality, spelling errors, productivity, syntactic complexity, and the use of narrative categories. A hierarchical regression analysis showed that, after eliminating the contributions of receptive vocabulary and productivity measures, only handwriting quality was significantly and positively related to the number of narrative categories utilized by children. In addition, the impact of the two transcription skills did not vary across grades. These findings suggest that, in a transparent language like Italian, spelling ability has a limited influence on the development of narrative competence in primary-school children (at least after the third grade). On the other hand, extensive practice in handwriting might have beneficial effects, by freeing resources that children might utilize to improve the structural organization of written narratives.
Journal Article
Imagining the Future City
Science fiction uses personal narratives and vivid images to create immersive experiences for the audience. Scientific scenarios, on the other hand, most often rely on predictive models that capture the key variables of the system being projected into the future. These two forms of foresight -- and the people who practice them -- typically don't engage with one another, but they should. Scientific scenarios are typically illustrated by an array of lines on a graph representing a range of possible futures; for example, possible changes in greenhouse gas emissions and atmospheric temperatures over the next several decades. Although such a spectrum of lines may reflect the results of sophisticated climate models, it is unlikely to communicate the information decision-makers need for strategizing and planning for the future. Even the most sophisticated models are simplifications of the forces influencing future outcomes. They present abstract findings, disconnected from local cultural, economic, or environmental conditions.
Journal Article
The silicon jungle
What happens when a naive intern is granted unfettered access to people's most private thoughts and actions? Young Stephen Thorpe lands a coveted internship at Ubatoo, an Internet empire that provides its users with popular online services, from a search engine and shopping to e-mail and social networking. When Stephen's boss asks him to work on a project with the American Coalition for Civil Liberties, Stephen innocently obliges, believing he is mining Ubatoo's vast databases to protect the ever-growing number of people unfairly targeted in the name of national security. But nothing is as it seems. Suspicious individuals--do-gooders, voyeurs, government agents, and radicals--surface, doing all they can to access the mass of desires and vulnerabilities gleaned from scouring Ubatoo's wealth of intimate information. Entry into Ubatoo's vaults of personal data need not require technical wizardry--simply knowing how to manipulate a well-intentioned intern may be enough.
Set in today's cutting-edge data mining industry,The Silicon Jungleis a cautionary tale of data mining's promise and peril, and how others can use our online activities for political and personal gain just as easily as for marketing and humanitarian purposes. A timely thriller,The Silicon Jungleraises serious ethical questions about today's technological innovations and how our most confidential activities and minute details can be routinely pieced together into rich profiles that reveal our habits, goals, and secret desires--all ready to be exploited in ways beyond our wildest imaginations.
eBook