Chess Database Research Articles

The Theoretical Model of Decision-Making Behaviour Geospatial Analysis Using Data Obtained from the Games of Chess.

The game of chess offers a conducive setting to explore basic cognitive processes, including decision-making. The game exercises analytical cause-and-effect thinking skills regardless of the level of play. Moreover, chess portals provide information on the chess games played and serve as a vast database. The numbers of games played thus have the potential to be analyzed comprehensively, including for purposes other than analyzing chess matches only. The primary objective of this study is to develop a methodology for using information obtained from chess games for geospatial social analysis. The assumption is that the methodology will allow for general geographical variation in personality inference in the future, relying on big data from chess databases. Future large-scale studies of the geographical differentiation of personality traits using the developed methodology may be applicable in a number of ways. The results can be used wherever cross-sectional social analyses are needed in the context of personality traits (decision-making) to better understand their geographical background. In turn, the geographical distribution of these traits is accompanied by a range of important social, educational, health, political and economic implications.

CHESS RESEARCH: RECENT TRENDS

Considerable research has been carried out on chess in the last seventy years. While classic research has centred on perception, memory, and decision making, contemporary research has focused on deliberate practice, individual differences, and education. Contrasting with classical research, which has mainly used experiments and computer modelling, more recent research has tended to use questionnaires, interviews, and analysis of computer databases as source of information. This article reviews these recent research trends, focusing on what has been learnt from chess research with respect to deliberate practice, intelligence, and transfer of skill. It also discusses ageing and risk taking between civilizations as examples of computer database analyses. Results clearly indicate that deliberate practice is a necessary, but not sufficient condition for achieving high levels of expertise. Other factors are important, some of which are innate. One of them is intelligence. Data show that chess players on average are more intelligent than individuals who do not play chess, and that chess skill positively correlates with intelligence. These results are unlikely to be explained by the hypothesis that chess leads to an increase of intelligence, as the results of experiments using chess instruction to bring about far-transfer effects are inconsistent. In addition, experiment designs used in chess instruction research are typically insufficient to allow strong conclusions about causality. Research using chess databases have led to interesting results, but its generalisability is likely to be limited. The article ends with recommendations for future research.

Vaccination and Non-Pharmaceutical Interventions: When Can the UK Relax About COVID-19?

Background: The announcement of efficacious vaccine candidates against SARS-CoV-2 has been met with worldwide acclaim and relief. Many countries already have detailed plans for vaccine targeting based on minimising severe illness, death and healthcare burdens. Normally, relatively simple relationships between epidemiological parameters, vaccine efficacy and vaccine uptake predict the success of any immunisation programme. However, the dynamics of vaccination against SARS-CoV-2 is made more complex by age-dependent factors, changing levels of infection and the potential relaxation of non-pharmaceutical interventions (NPIs) as the perceived risk declines. Methods: In this study we use an age-structured mathematical model, matched to a range of epidemiological data, which also captures the roll-out of a two-dose vaccination programme targeted at specific age groups. Findings: We consider the interaction between the UK vaccination programme and future re- laxation (or removal) of NPIs. Our predictions highlight the population-level risks of early relaxation leading to a pronounced wave of infection, hospital admissions and deaths. Only vaccines that offer high transmission-blocking efficacy with high uptake in the general population allow relaxation of NPIs without a huge surge in deaths. Interpretation: While the novel vaccines against SARS-CoV-2 offer a potential exit strategy for this outbreak, this is highly contingent on the transmission blocking action of the vaccine and the population uptake, both of which need to be carefully monitored as vaccine programmes are rolled out in the UK and other countries.Funding Statement: This research was funded by the National Institute for Health Research (NIHR) [Policy Research Programme, Mathematical & Economic Modelling for Vaccination and Immunisation Evaluation, and Emergency Response; NIHR200411], the Medical Research Council through the COVID- 19 Rapid Response Rolling Call [grant number MR/V009761/1] and through the JUNIPER modelling consortium [grant number EP/V030477/1]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Declaration of Interests: All authors declare that they have no competing interests.Ethics Approval Statement: The data were supplied from the CHESS database after anonymisation under strict data protection protocols agreed between the University of Warwick and Public Health England. The ethics of the use of these data for these purposes was agreed by Public Health England with the Government’s SPI-M(O) / SAGE committees.

Precautionary Breaks: Planned, Limited Duration Circuit Breaks to Control the Prevalence of COVID-19

Background: The COVID-19 pandemic in the UK has been characterised by periods of exponential growth and decline, as different non-pharmaceutical interventions (NPIs) are brought into play. During the early uncontrolled phase of the outbreak (early March 2020) there was a period of prolonged exponential growth with epidemiological observations such as hospitalisation doubling every 3-4 days. The enforcement of strict lockdown measures led to a noticeable decline in all epidemic quantities, that slowed during the summer as control measures were relaxed. Since August, infections, hospitalisations and deaths have been rising (precise estimation of the current growth rate is difficult due to extreme regional heterogeneity and temporal lags between the different epidemiological observations) and various NPIs have been applied locally throughout the UK in response.Methods: We investigate the impact of planned, limited duration periods of strict NPIs using simple models of exponential growth or decline and more complex age-structured models matched to a variety of data sources. We simulate the impact of two weeks of sustained control against a variety of intrinsic growth rates. We consider the impact of these breaks on the prevalence of infection, as well as the total number of predicted hospitalisations and deaths.Findings: Precautionary breaks are characterised by both a relative decline in infection and the temporal reset - how far back in time since this reduced level of cases was previously observed. Precautionary breaks provide the biggest temporal gains when the growth rate is low, but offer a much needed reduction to increasing infection when the growth rate is higher. Although not a long-term solution, an intense two-week break can reduce the number of deaths or hospitalisations by around 50% in the short-term.Interpretation The planned nature, and finite duration, of precautionary breaks, may lessen their social and economic impact, while by driving infection to lower levels they may allow resource limited controls (such as test, trace and isolated) to be more effective.Funding Statement: This work has been supported by the Engineering and Physical Sciences Research Council through the MathSys CDT [grant number EP/S022244/1], the Medical Research Council through the COVID-19 Rapid Response Rolling Call [grant number MR/V009761/1], and the Biotechnology and Biological sciences Research Council through the Midlands Integrative Biosciences Training Partnership (MIBTP) [grant number BB/M01116X/1].Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: Data from the CHESS database were supplied after anonymisation under strict data protection protocols agreed between the University of Warwick and Public Health England. The ethics of the use of these data for these purposes was agreed by Public Health England with the Government’s SPI-M(O) / SAGE committees.

Tracy–Singh Product and Genetic Whale Optimization Algorithm for Retrievable Data Perturbation for Privacy Preserved Data Publishing in Cloud Computing

Abstract This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud computing. Initially, genetic whale optimization algorithm (genetic WOA) is developed by integrating genetic algorithm (GA) and WOA for generating the optimized secret key. Then, the input data and the optimized secret key are given to the Tracy–Singh product-based model for transforming the original database into perturbed database. Finally, the perturbed database can be retrieved by the client, if and only if the client knows the secret key. The performance of the proposed model is analyzed using three databases, namely, chess, T10I4D100K and retail databases from the FIMI data set based on the performance metrics, privacy and utility. Also, the proposed model is compared with the existing methods, such as Retrievable General Additive Data Perturbation, GA and WOA, for the key values 128 and 256. For the key value 128, the proposed model has the better privacy and utility of 0.18 and 0.83 while using the chess database. For the key value 256, the proposed model has the better privacy and utility of 0.18 and 0.85, using retail database. From the analysis, it can be shown that the proposed model has better privacy and utility values than the existing models.

CHESS: a new human gene catalog curated from thousands of large-scale RNA sequencing experiments reveals extensive transcriptional noise

We assembled the sequences from deep RNA sequencing experiments by the Genotype-Tissue Expression (GTEx) project, to create a new catalog of human genes and transcripts, called CHESS. The new database contains 42,611 genes, of which 20,352 are potentially protein-coding and 22,259 are noncoding, and a total of 323,258 transcripts. These include 224 novel protein-coding genes and 116,156 novel transcripts. We detected over 30 million additional transcripts at more than 650,000 genomic loci, nearly all of which are likely nonfunctional, revealing a heretofore unappreciated amount of transcriptional noise in human cells. The CHESS database is available at http://ccb.jhu.edu/chess.

Brain storm-based Whale Optimization Algorithm for privacy-protected data publishing in cloud computing

Cloud computing serves as a major boost for the digital era since it handles data from a large number of users simultaneously. Besides the several useful characteristics, providing security to the data stored in the cloud platform is a major challenge for the service providers. Privacy preservation schemes introduced in the literature trying to enhance the privacy and utility of the data structures by modifying the database with the secret key. In this paper, an optimization scheme, Brain Storm based Whale Optimization Algorithm (BS-WOA), is introduced for identifying the secret key. The database from the data owner is modified with the optimal secret key for constructing the retrievable perturbation data for preserving the privacy and utility. The proposed BS-WOA is designed through the hybridization of Brain Storm Optimization and Whale Optimization Algorithm. Simulation of the proposed technique with the BS-WOA is done in the three standard databases, such as chess T10I4D100 K, and the retail databases. When evaluated for the key size of 256, the proposed BS-WOA achieved privacy value of 0.186 and utility value of 0.8777 for the chess database, and thus, has improved performance.

Structure constrained by metadata in networks of chess players

Chess is an emblematic sport that stands out because of its age, popularity and complexity. It has served to study human behavior from the perspective of a wide number of disciplines, from cognitive skills such as memory and learning, to aspects like innovation and decision-making. Given that an extensive documentation of chess games played throughout history is available, it is possible to perform detailed and statistically significant studies about this sport. Here we use one of the most extensive chess databases in the world to construct two networks of chess players. One of the networks includes games that were played over-the-board and the other contains games played on the Internet. We study the main topological characteristics of the networks, such as degree distribution and correlations, transitivity and community structure. We complement the structural analysis by incorporating players’ level of play as node metadata. Although both networks are topologically different, we show that in both cases players gather in communities according to their expertise and that an emergent rich-club structure, composed by the top-rated players, is also present.

Chess knowledge predicts chess memory even after controlling for chess experience: Evidence for the role of high-level processes.

The expertise effect in memory for chess positions is one of the most robust effects in cognitive psychology. One explanation of this effect is that chess recall is based on the recognition of familiar patterns and that experts have learned more and larger patterns. Template theory and its instantiation as a computational model are based on this explanation. An alternative explanation is that the expertise effect is due, in part, to stronger players having better and more conceptual knowledge, with this knowledge facilitating memory performance. Our literature review supports the latter view. In our experiment, a sample of 79 chess players were given a test of memory for chess positions, a test of declarative chess knowledge, a test of fluid intelligence, and a questionnaire concerning the amount of time they had played nontournament chess and the amount of time they had studied chess. We determined the numbers of tournament games the players had played from chess databases. Chess knowledge correlated .67 with chess memory and accounted for 16% of the variance after controlling for chess experience. Fluid intelligence accounted for an additional 13% of the variance. These results support the conclusion that both high-level conceptual processing and low-level recognition of familiar patterns play important roles in memory for chess positions.

A Study of Memory Effects in a Chess Database.

A series of recent works studying a database of chronologically sorted chess games–containing 1.4 million games played by humans between 1998 and 2007– have shown that the popularity distribution of chess game-lines follows a Zipf’s law, and that time series inferred from the sequences of those game-lines exhibit long-range memory effects. The presence of Zipf’s law together with long-range memory effects was observed in several systems, however, the simultaneous emergence of these two phenomena were always studied separately up to now. In this work, by making use of a variant of the Yule-Simon preferential growth model, introduced by Cattuto et al., we provide an explanation for the simultaneous emergence of Zipf’s law and long-range correlations memory effects in a chess database. We find that Cattuto’s Model (CM) is able to reproduce both, Zipf’s law and the long-range correlations, including size-dependent scaling of the Hurst exponent for the corresponding time series. CM allows an explanation for the simultaneous emergence of these two phenomena via a preferential growth dynamics, including a memory kernel, in the popularity distribution of chess game-lines. This mechanism results in an aging process in the chess game-line choice as the database grows. Moreover, we find burstiness in the activity of subsets of the most active players, although the aggregated activity of the pool of players displays inter-event times without burstiness. We show that CM is not able to produce time series with bursty behavior providing evidence that burstiness is not required for the explanation of the long-range correlation effects in the chess database. Our results provide further evidence favoring the hypothesis that long-range correlations effects are a consequence of the aging of game-lines and not burstiness, and shed light on the mechanism that operates in the simultaneous emergence of Zipf’s law and long-range correlations in a community of chess players.

Chess databases as a research vehicle in psychology: Modeling large data

The game of chess has often been used for psychological investigations, particularly in cognitive science. The clear-cut rules and well-defined environment of chess provide a model for investigations of basic cognitive processes, such as perception, memory, and problem solving, while the precise rating system for the measurement of skill has enabled investigations of individual differences and expertise-related effects. In the present study, we focus on another appealing feature of chess—namely, the large archive databases associated with the game. The German national chess database presented in this study represents a fruitful ground for the investigation of multiple longitudinal research questions, since it collects the data of over 130,000 players and spans over 25 years. The German chess database collects the data of all players, including hobby players, and all tournaments played. This results in a rich and complete collection of the skill, age, and activity of the whole population of chess players in Germany. The database therefore complements the commonly used expertise approach in cognitive science by opening up new possibilities for the investigation of multiple factors that underlie expertise and skill acquisition. Since large datasets are not common in psychology, their introduction also raises the question of optimal and efficient statistical analysis. We offer the database for download and illustrate how it can be used by providing concrete examples and a step-by-step tutorial using different statistical analyses on a range of topics, including skill development over the lifetime, birth cohort effects, effects of activity and inactivity on skill, and gender differences.

Is age really cruel to experts? Compensatory effects of activity.

Age-related decline may not be as pronounced in complex activities as it is in basic cognitive processes, but ability deterioration with age is difficult to deny. However, studies disagree on whether age is kinder to more able people than it is to their less able peers. In this article, we investigated the "age is kinder to the more able" hypothesis by using a chess database that contains activity records for both beginners and world-class players. The descriptive data suggested that the skill function across age captures the 3 phases as described in Simonton's model of career trajectories: initial rise to the peak of performance, postpeak decline, and eventual stabilization of decline. We therefore modeled the data with a linear mixed-effect model using the cubic function that captures 3 phases. The results show that age may be kind to the more able in a subtler manner than has previously been assumed. After reaching the peak at around 38 years, the more able players deteriorated more quickly. Their decline, however, started to slow down at around 52 years, earlier than for less able players (57 years). Both the decline and its stabilization were significantly influenced by activity. The more players engaged in playing tournaments, the less they declined and the earlier they started to stabilize. The best experts may not be immune to aging, but their previously acquired expertise and current activity enable them to maintain high levels of skill even at an advanced age.

Memory and long-range correlations in chess games

In this paper we report the existence of long-range memory in the opening moves of a chronologically ordered set of chess games using an extensive chess database. We used two mapping rules to build discrete time series and analyzed them using two methods for detecting long-range correlations; rescaled range analysis and detrended fluctuation analysis. We found that long-range memory is related to the level of the players. When the database is filtered according to player levels we found differences in the persistence of the different subsets. For high level players, correlations are stronger at long time scales; whereas in intermediate and low level players they reach the maximum value at shorter time scales. This can be interpreted as a signature of the different strategies used by players with different levels of expertise. These results are robust against the assignation rules and the method employed in the analysis of the time series.

Measuring Chess Experts' Single-Use Sequence Knowledge: An Archival Study of Departure from ‘Theoretical’ Openings

The respective roles of knowledge and search have received considerable attention in the literature on expertise. However, most of the evidence on knowledge has been indirect – e.g., by inferring the presence of chunks in long-term memory from performance in memory recall tasks. Here we provide direct estimates of the amount of monochrestic (single use) and rote knowledge held by chess players of varying skill levels. From a large chess database, we analyzed 76,562 games played in 2008 by individuals ranging from Class B players (average players) to Masters to measure the extent to which players deviate from previously known initial sequences of moves (“openings”). Substantial differences were found in the number of moves known by players of different skill levels, with more expert players knowing more moves. Combined with assumptions independently made about the branching factor in master games, we estimate that masters have memorized about 100,000 opening moves. Our results support the hypothesis that monochrestic knowledge is essential for reaching high levels of expertise in chess. They provide a direct, quantitative estimate of the number of opening moves that players have to know to reach master level.

Data-Mining Chess Databases

This is a report on the data-mining of two chess databases, the objective being to compare their sub-7-man content with perfect play as documented in Nalimov endgame tables. Van der Heijden’s ENDGAME STUDY DATABASE IV is a definitive collection of 76,132 studies in which White should have an essentially unique route to the stipulated goal. Chessbase’s BIG DATABASE 2010 holds some 4.5 million games. Insight gained into both database content and data-mining has led to some delightful surprises and created a further agenda.

Zipf’s Law in the Popularity Distribution of Chess Openings

We perform a quantitative analysis of extensive chess databases and show that the frequencies of opening moves are distributed according to a power law with an exponent that increases linearly with the game depth, whereas the pooled distribution of all opening weights follows Zipf's law with universal exponent. We propose a simple stochastic process that is able to capture the observed playing statistics and show that the Zipf law arises from the self-similar nature of the game tree of chess. Thus, in the case of hierarchical fragmentation the scaling is truly universal and independent of a particular generating mechanism. Our findings are of relevance in general processes with composite decisions.

Evaluation of Economy in a Zero-sum Perfect Information Game

A zero-sum perfect information game is one where every player knows all the moves. Chess is a good example where the object is to checkmate or capture the enemy king. One important feature of checkmates, especially in chess problem composition, is economy. This paper proposes a computational function to evaluate the economy of checkmate configurations on the chessboard. Several experiments were performed comparing chess compositions and regular games to validate the function. The results suggest that the proposed evaluation function is able to correctly discern economical differences in checkmate positions to a high degree of statistical significance and correlates positively with the perception of human chess players. This evaluation function can therefore be useful in increasing the versatility of chess database search engines, as a component in aesthetic models of chess and aiding judges in chess composition tournaments.

Short-term nitrogen dioxide exposure and acute respiratory disease in children.

A CHESS data base from Chattanooga, Tennessee was thoroughly scrutinized and found to be of high enough quality to warrant epidemiological analysis. Using this data base, the relationship between NO2 ambient pollution levels and acute respiratory disease in children was examined. Although a statistically significant relationship was found, it was not monotonic. Indeed, over the range of pollution values experiences, more illness is associated with low pollution values than with high ones. A U-shaped relationship between illness and NO2 concentrations was found in several subpopulations in addition to the entire data set, although for some subpopulations no relationship was found. In contrast, higher ambient sulfate levels were found to have a positive effect on acute respiratory disease incidence in children over the entire period and for different subsamples, although this effect was not significant for either season analyzed separately.