Generative design systems produce myriad design alternatives that comply with stated requirements. Since generative design systems yield the greatest benefits during conceptual design, requirements are often ambiguous and are comprised of mixed variables (e.g., categorical, continuous, etc.), which leads to a generative design solution space with a plethora of options for the user to review. With a plethora of design alternatives to choose from, many of which are similar, increased user workload leads to inefficient design selection processes. Subsequently, inefficient design selection processes could result in a negative user experience and improper design alternative selection. Therefore, it is imperative that generative design systems leverage parsing methods that methodologically reduce the quantity of design options that are presented to the user, while retaining novel designs from distinct solution space regions. Although parsing a solution space can yield smaller subsets of design alternatives, it is also imperative to consider how the subsets are presented to the user. A user study (N=49) was performed to evaluate user performance, workload, and experience during a generative design selection process, given manipulation of both the quantity and filtering of parsed subsets of alternatives. Subsets were filtered using cluster analysis using one of seven parameters, where participants experienced two filters across seven iterations each. Results show that cognitive workload is reduced when a design solution space consists of 50 to 100 design alternatives, with a clustering parsing method that considers all design alternative variables. Study findings can further be applied to other domains where a user is presented with a plethora of alternative options, requiring a method for improving the decision-making process.

Human economic activities are inherently embedded in social networks. Nevertheless, whether social media information can affect short-term housing price changes, one of the most fundamental economic elements in modern economies, remains unclear. In this paper, we empirically investigate the effect of public expectations expressed on social media on the short-term housing price fluctuations of cities in China. The data were collected from Sina Weibo, one of the largest Twitter-like services in China. We first use a lexicon-based method to mine public expectations of housing price on Sina Weibo, and then use panel econometric models to empirically verify whether the public expectations on Sina Weibo can help more effectively explain short-term housing price changes of cities in China. Our results suggest that housing price expectations expressed on social media have a positive effect on housing price changes; that is, a 0.1 increase in bullish expectations on social media will result in a 0.2% increase in the housing price growth rate monthly but lagged by two months. The results are robust after additional tests. Our results are theoretically and empirically consistent with the findings of behavioural economics in emphasizing the importance of expectations and the failure of economic fundamentals in explaining the short-term changes of urban housing prices, which can not only shed light on the amplifying role of social media information on housing price changes, but also help investors use information technologies to assist their investment decision-makings.

The grey wolf optimizer(GWO), a population-based meta-heuristic algorithm, mimics the predatory behavior of grey wolf packs. Continuously exploring and introducing improvement mechanisms is one of the keys to drive the development and application of GWO algorithms. To overcome the premature and stagnation of GWO, the paper proposes a multiple strategy grey wolf optimization algorithm (MSGWO). Firstly, an variable weights strategy is proposed to improve convergence rate by adjusting the weights dynamically. Secondly, this paper proposes a reverse learning strategy, which randomly reverses some individuals to improve the global search ability. Thirdly, the chain predation strategy is designed to allow the search agent to be guided by both the best individual and the previous individual. Finally, this paper proposes a rotation predation strategy, which regards the position of the current best individual as the pivot and rotate other members for enhacing the exploitation ability. To verify the performance of the proposed technique, MSGWO is compared with seven state-of-the-art meta-heuristics and four variant GWO algorithms on CEC2022 benchmark functions and three engineering optimization problems. The results demonstrate that MSGWO has better performance on most of benchmark functions and shows competitive in solving engineering design problems.

In this paper, we combine an agent-based model of multi-asset stock market with circuit breaker mechanism and empirical analysis of S&P 500 Index to study market behaviors under the circuit breaker. The artificial stock market model can reproduce the stylized fact that the stock index triggers a circuit breaker. The results show that the smaller the circuit breaker, the more likely the circuit breaker events will occur. And the higher the traders’ index-dependent strength, the more likely the circuit breaker events will occur. From the perspective of market behaviors under the stock index circuit breaker, we find that the market volatility, the correlation of individual stock returns and the convergence of traders’ behavior on the circuit breaker day are higher than those before the circuit breaker day when the circuit breaker in the market is set relatively small or traders refer to the stock index more for decision-making. This is because the smaller circuit breaker mechanism and traders’ more reference to the stock index for decision-making make the behavior of originally heterogeneous traders in the market converge, which aggravates the occurrence of circuit breakers.