Authors:Jovan Njegić, Dejan Živkov, and Jelena Damnjanović
This paper strives to investigate the level of business cycles synchronisation between 8 Central and Eastern European Countries (CEEC) and the EU-15. We use wavelet coherence and phase difference methodology as a very suitable tool that observes simultaneously the strength of business cycles’ co-movement in the aspect of time as well as in the aspect of frequency. The results indicate that the business cycles of CEECs are generally synchronised with the EU-15 business cycles, whereas distinct differences existed before, during, and after the financial crisis (2008–2009) and during the European sovereign debt crisis (2010–2011). In other words, we demonstrate that very strong business cycles synchronisation occurred in almost all CEECs during crisis periods and at higher wavelet scales, while only moderate synchronisation is recorded in relatively tranquil periods at higher frequencies. The results suggest that smaller CEECs, but also larger countries such as the Czech Republic, Hungary, and to some extent Slovakia as well have a higher level of business cycles synchronisation with the EU-15, particularly in the crisis period at short-run as well as at long-run fluctuations. However, we do not find strong business cycles co-movement in cases of Poland and Latvia via HP and BP filters at higher frequencies during the crisis, which might indicate a higher resistance of these countries to external systemic shocks.
With flexible (variable) retirement every individual determines his optimal retirement age, depending on a common benefit-retirement age schedule and his life expectancy. The government maximises the average expected lifetime utility minus a scalar multiple of the variance of the lifetime pension balances to achieve harmony between the maximisation of welfare and the minimisation of redistribution. Since the government cannot identify types by life expectancy, it must take the individual incentive compatibility constraints into account. Second-best schedules strongly reduce the variances of benefits and of retirement ages of the so-called actuarially fair system, thus achieving higher social welfare and lower redistribution.
Authors:Sungchul Choi, Janghyeok Yoon, Kwangsoo Kim, Jae Yeol Lee, and Cheol-Han Kim
This paper suggests a method for Subject–Action–Object (SAO) network analysis of patents for technology trends identification by using the concept of function. The proposed method solves the shortcoming of the keyword-based approach to identification of technology trends, i.e., that it cannot represent how technologies are used or for what purpose. The concept of function provides information on how a technology is used and how it interacts with other technologies; the keyword-based approach does not provide such information. The proposed method uses an SAO model and represents “key concept” instead of “key word”. We present a procedure that formulates an SAO network by using SAO models extracted from patent documents, and a method that applies actor network theory to analyze technology implications of the SAO network. To demonstrate the effectiveness of the SAO network this paper presents a case study of patents related to Polymer Electrolyte Membrane technology in Proton Exchange Membrane Fuel Cells.
In the competitive business environment, early identification of technological opportunities is crucial for technology strategy formulation and research and development planning. There exist previous studies that identify technological directions or areas from a broad view for technological opportunities, while few studies have researched a way to detect distinctive patents that can act as new technological opportunities at the individual patent level. This paper proposes a method of detecting new technological opportunities by using subject–action–object (SAO)-based semantic patent analysis and outlier detection. SAO structures are syntactically ordered sentences that can be automatically extracted by natural language processing of patent text; they explicitly show the structural relationships among technological components in a patent, and thus encode key findings of inventions and the expertise of inventors. Therefore, the proposed method allows quantification of structural dissimilarities among patents. We use outlier detection to identify unusual or distinctive patents in a given technology area; some of these outlier patents may represent new technological opportunities. The proposed method is illustrated using patents related to organic photovoltaic cells. We expect that this method can be incorporated into the research and development process for early identification of technological opportunities.
Authors:Janghyeok Yoon, Sungchul Choi, and Kwangsoo Kim
Technology analysis is a process which uses textual analysis to detect trends in technological innovation. Co-word analysis (CWA), a popular method for technology analysis, encompasses (1) defining a set of keyword or key phrase patterns which are represented in technology-dependent terms, (2) generating a network that codifies the relations between occurrences of keywords or key phrases, and (3) identifying specific trends from the network. However, defining the set of keyword or key phrase patterns heavily relies on effort of experts, who may be expensive or unavailable. Furthermore defining keyword or key phrase patterns of new or emerging technology areas may be a difficult task even for experts. To solve the limitation in CWA, this research adopts a property-function based approach. The property is a specific characteristic of a product, and is usually described using adjectives; the function is a useful action of a product, and is usually described using verbs. Properties and functions represent the innovation concepts of a system, so they show innovation directions in a given technology. The proposed methodology automatically extracts properties and functions from patents using natural language processing. Using properties and functions as nodes, and co-occurrences as links, an invention property-function network (IPFN) can be generated. Using social network analysis, the methodology analyzes technological implications of indicators in the IPFN. Therefore, without predefining keyword or key phrase patterns, the methodology assists experts to more concentrate on their knowledge services that identify trends in technological innovation from patents. The methodology is illustrated using a case study of patents related to silicon-based thin film solar cells.
Patents constitute an up-to-date source of competitive intelligence in technological development; thus, patent analysis has been a vital tool for identifying technological trends. Patent citation analysis is easy to use, but fundamentally has two main limitations: (1) new patents tend to be less cited than old ones and may miss citations to contemporary patents; (2) citation-based analysis cannot be used for patents in databases which do not require citations. Naturally, citation-based analysis tends to underestimate the importance of new patents and may not work in rapidly-evolving industries in which technology life-cycles are shortening and new inventions are increasingly patented world-wide. As a remedy, this paper proposes a patent network based on semantic patent analysis using subject-action-object (SAO) structures. SAO structures represent the explicit relationships among components used in a patent, and are considered to represent key concepts of the patent or the expertise of the inventor. Based on the internal similarities between patents, the patent network provides the up-to-date status of a given technology. Furthermore, this paper suggests new indices to identify the technological importance of patents, the characteristics of patent clusters, and the technological capabilities of competitors. The proposed method is illustrated using patents related to synthesis of carbon nanotubes. We expect that the proposed procedure and analysis will be incorporated into technology planning processes to assist experts such as researchers and R&D policy makers in rapidly-evolving industries.
Agent-based simulation can model simple micro-level mechanisms capable of generating macro-level patterns, such as frequency distributions and network structures found in bibliometric data. Agent-based simulations of organisational learning have provided analogies for collective problem solving by boundedly rational agents employing heuristics. This paper brings these two areas together in one model of knowledge seeking through scientific publication. It describes a computer simulation in which academic papers are generated with authors, references, contents, and an extrinsic value, and must pass through peer review to become published. We demonstrate that the model can fit bibliometric data for a token journal, Research Policy. Different practices for generating authors and references produce different distributions of papers per author and citations per paper, including the scale-free distributions typical of cumulative advantage processes. We also demonstrate the model's ability to simulate collective learning or problem solving, for which we use Kauffman's NK fitness landscape. The model provides evidence that those practices leading to cumulative advantage in citations, that is, papers with many citations becoming even more cited, do not improve scientists’ ability to find good solutions to scientific problems, compared to those practices that ignore past citations. By contrast, what does make a difference is referring only to publications that have successfully passed peer review. Citation practice is one of many issues that a simulation model of science can address when the data-rich literature on scientometrics is connected to the analogy-rich literature on organisations and heuristic search.
János Kornai’s Anti-Equilibrium was ahead of its time when it was written, and even today, when system dynamics software is extensively used in engineering and other disciplines, it remains ahead of its time in economics, which is still hampered by the dominant equilibrium-dominated modeling approach and the subjective beliefs that the truly sterile state of equilibrium is preferable to the real dynamic instability of actual capitalism. I show that the dead-end in which economic theory finds itself today is easily escaped if we adopt the system dynamics approach Kornai recommended in 1971, and derive macroeconomics directly from the structure of the macroeconomy.
Authors:Hyunseok Park, Janghyeok Yoon, and Kwangsoo Kim
Companies should investigate possible patent infringement and cope with potential risks because patent litigation may have a tremendous financial impact. An important factor to identify the possibility of patent infringement is the technological similarity among patents, so this paper considered technological similarity as a criterion for judging the possibility of infringement. Technological similarities can be measured by transforming patent documents into abstracted forms which contain specific technological key-findings and structural relationships among technological components in the invention. Although keyword-based technological similarity has been widely adopted for patent analysis related research, it is inadequate for identifying patent infringement because a keyword vector cannot reflect specific technological key-findings and structural relationships among technological components. As a remedy, this paper exploited a subject–action–object (SAO) based semantic technological similarity. An SAO structure explicitly describes the structural relationships among technological components in the patent, and the set of SAO structures is considered to be a detailed picture of the inventor's expertise, which is the specific key-findings in the patent. Therefore, an SAO based semantic technological similarity can identify patent infringement. Semantic similarity between SAO structures is automatically measured using SAO based semantic similarity measurement method using WordNet, and the technological relationships among patents were mapped onto a 2-dimensional space using multidimensional scaling (MDS). Furthermore, a clustering algorithm is used to automatically suggest possible patent infringement cases, allowing large sets of patents to be handled with minimal effort by human experts. The proposed method will be verified by detecting real patent infringement in prostate cancer treatment technology, and we expect this method to relieve human experts’ work in identifying patent infringement.
When calculating different profitability measures for a life insurance company, one of the most important parameters to know is the probability of a policy being in force at any given time after the start of risk bearing. These probabilities are given by the survival function. In this paper, we examine data from a Hungarian insurance company, in order to build models for the survival functions of two life insurance products. For survival function estimation based on the unique parameters of a new policy, Cox regression is used. However, not all parameters of a new policy are relevant in estimating the survival function. Therefore, application of model selection algorithms is needed. Furthermore, if the exact effects of the policy parameters for the survival function can be determined, the insurance company can direct its sales team to acquire policies with positive technical results. When traditional model selection techniques proposed by the literature (such as best subset, stepwise and regularization methods) are applied on our data, we find that the effect of the selected predictors for survival cannot be determined, as there is a harmful degree of multicollinearity. In order to tackle this problem, we propose adding the hybrid metaheuristic from Láng et al. (2017) to the Cox regression in order to eliminate multicollinearity from the final model. On the test sets, performance of the models from the metaheuristic rivals those of the traditional algorithms with the use of noticeably less predictors. These predictors are not significantly correlated and are significant for survival, as well. It is shown in the paper that with the application of metaheuristics, we could produce a model with good predicting capabilities and interpretable predictor effects. These predictor effects can be used to direct the sales activities of the insurance company.