Incentives are frequently used by governments and employers to encourage cooperation. Here, we investigated the effect of centralized incentives on cooperation, firstly in a behavioral study and then replicated in a subsequent neuroimaging (fMRI) study. In both studies, participants completed a novel version of the Public Goods Game, including experimental conditions in which the administration of centralized incentives was probabilistic and incentives were either of a financial or social nature. Behavioral results showed that the prospect of potentially receiving financial and social incentives significantly increased cooperation, with financial incentives yielding the strongest effect. Neuroimaging results showed that activation in the bilateral lateral orbitofrontal cortex and precuneus increased when participants were informed that incentives would be absent versus when they were present. Furthermore, activation in the medial orbitofrontal cortex increased when participants would potentially receive a social versus a financial incentive. These results speak to the efficacy of different types of centralized incentives in increasing cooperative behavior, and they show that incentives directly impact the neural mechanisms underlying cooperation.
Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
