Commitments have been shown to promote cooperation if, on the one hand, they can be sufficiently enforced and on the other hand, the cost of arranging them is justified with respect to the benefits of cooperation. When either of these constraints is not met it leads to the prevalence of commitment free-riders, such as those who commit only when someone else pays to arrange the commitments. Here, we show how intention recognition may circumvent such weakness of costly commitments. We describe an evolutionary model, in the context of the one-shot Prisoner's Dilemma, showing that if players first predict the intentions of their co-player and propose a commitment only when they are not confident enough about their prediction, the chances of reaching mutual cooperation are largely enhanced. We find that an advantageous synergy between intention recognition and costly commitments depends strongly on the confidence and accuracy of intention recognition. In general, we observe an intermediate level of confidence threshold leading to the highest evolutionary advantage, showing that neither unconditional use of commitment nor intention recognition can perform optimally. Rather, our results show that arranging commitments is not always desirable, but that they may be also unavoidable depending on the strength of the dilemma.
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Several behavioral experiments on intention based strategies exist that are closely related to our model. The experiment in Ref. 26 uses a sequential PD (in the presence of noise) where the second-moving player can recognize the first-moving player's intention and choose whether to punish a defecting act. The experiment showed that individuals tend to use strong punishment against those who are recognized to have a clear intention of defection while no (or weak) punishment is used against those who defected but the act is recognized to be unintentional. Our work differs from this experimental setting in that the intention recognition process is done prior to the interaction (to find out whether it is necessary to arrange prior commitments), while it is posterior in the experiment, i.e. after the move has been made. Another experiment in Ref. 21 showed that, in the course if the repeated Prisoner's Dilemma, if co-players' intention can be observed, it significantly fosters cooperation since unintentional defection caused by noise can be forgiven, as also shown theoretically in Ref. 22. Note that both experiments have been designed so that the intention recognition process is facilitated, thereby guaranteeing a high confidence level. In such cases, as shown in the present work, the synergy of intention recognition and commitments, both aiming at clarifying co-players' intention, can promote a high level of cooperation.
Several extensions to the present model can be described. In our model we have considered a general one-shot interaction scenario, but we envisage that as more prior experience is incorporated, for instance by observing direct or indirect past actions of the co-player, intention recognition can be performed better, thereby leading to better performance of IRCOM. Indeed, in Refs. 22, 34, in the context of the repeated PD with implementation noise, Artificial Intelligence based intention recognition strategies35,36 can more accurately assess a co-player's intention whenever more past interactions are taken into account. In SI, we consider a more effective IRCOM strategy, having a more accurate intention recognition capability (see Figure S3). Our numerical results show that, whenever the intention recognition model is efficient enough, the intention recognition strategy by itself alone (i.e. IRCOM with θ = 0) performs quite well, complying with the results obtained in Refs. 22, 34, where concrete intention recognition models are deployed.
A less obvious development is the shift from acute sites of care, which have lower margins than most other sites of care outside of the hospital. Non-acute sites have lower costs and EBITDA margins two to three times higher than the acute care setting. The pandemic has driven the shift to non-acute settings, given the hospital backlog and patient and doctor preference for more convenient and virtual care. We have also seen underlying business shifts such as the accelerated adoption of value-based care. Many value-based players could deliver lower costs and better outcomes as well as realize margins of more than 15 percent in primary care and specialty models.
Traditional drug dispensers such as retail and mail pharmacies continue to face margin pressure, leading to a contraction of profit pools. New technology-enabled pharmacies have emerged, featuring direct-to-consumer models with digital prescription management, automated workflows, and faster home delivery services. Although these players have not yet reached substantial market share, they are growing quickly, spurred by substantial private equity investment. Competition from these players could promote innovation around convenience and experience in the business models of larger retail and mail pharmacies as well, creating potential margin upside.
Outside of overall specialty growth, the pharmacy benefit managers (PBMs) segment is under pressure for more transparency into rebates and network spread pricing from payers and sponsors. Employer demand for cost containment and predictability has given rise to a wave of new and innovative pricing models. Specialized players (such as specialty drug managers, therapy management, benefit optimizers, and pharmacy benefit administrators) are now taking on some PBM functions. In the face of these trends and commoditization, PBMs have launched group purchasing organizations (for example, Ascent Health Services, Zinc Health Services, and Emisar Pharma Services) to better negotiate with drug manufacturers. They continue to invest to improve employer and employee experience and ramp up efforts to better manage medical benefit specialty drugs.
The COVID-19 pandemic has had a profound effect on the healthcare industry, from shifting profit pools to a spike in innovation to the creation of new business models. Payers, providers, HST players, and pharma services firms are facing big decisions about what kind of companies they want to be in the coming years. Even as the pandemic continues, now is the time to make strategic choices and potentially big bets. Acquisitions and new business building will increasingly be key success factors.
Our model was applied to address open questions in circadian rhythm biology: firstly, what are the possible reasons for the observed two-loop design? Mathematically, one negative feedback loop with a time delay would be enough to generate stable oscillations. There is evidence from published data showing that overexpression of components of the PC loop does not destroy oscillations [44], [45] which together with remarkable phenotypic effects for members of the RBR loop [46] motivated us to investigate the role of the RBR loop in detail. Secondly, how does degradation kinetics affect the period? We emphasize that such questions cannot be answered intuitively but require quantitative models. The period of the system depends on the timing of gene expression, accumulation and decay, and since clock protein degradation can influence all these processes, intuitive predictions are difficult.
Few studies, excluding strictly statistical models, have investigated the use of baseline molecular data for cell-specific model calibration, e.g., molecular data like gene-expression solely from an unperturbed system (Flobak et al., 2015; Silverbush et al., 2017; Béal et al., 2019). Silverbush et al. (2017) used their model, initially informed with perturbation data to a specific cell line, to predict combinations for another cell line using only mutation data, validating the majority of the predicted combinations. In a recent DREAM challenge (Menden et al., 2019), participating teams used different machine-learning techniques to predict combination effects for 910 drug combinations using baseline molecular data across multiple cancer cell lines from different origins. In contrast, Jaeger et al. (2017) have explored the use of a generic signaling network without incorporating any cell-specific activity data. They reported a strong correlation between molecular features, such as mutations status and molecular subtype, and synergy strength in their validation data set, suggesting that predictive models can be generated without depending on perturbation data for model training.
Several different reference models have been developed to quantify synergism. Each of these models have different definitions and assumptions and as such they can show disagreement in synergy scoring (Vlot et al., 2019). In this study we have applied the HSA synergy metric to allow for the detection of as many as possible potentially synergistic drug combinations for pre-clinical screening. In order to binarized the drug combination data a synergy cut-off guided by literature and investigation of drug responses was applied, similar to other studies (Menden et al., 2019). When testing two alternative cut-offs that would result in approximately half or twice as high synergy calling compared to the one used in this study, we observe a generally increase in sensitivity with the more conservative experimental threshold, reflected by a decrease in FN prediction (Supplementary Figure 7 and Supplementary Table 27). This indicates that our model predictions were enriched in the strongest synergistic combinations. To enable a more fine-graded response of synergy strength, inclusion of additional multi-valued nodes or the use of a multi-valued logical model approach such as applied by Silverbush et al. (2017) may be investigated in future explorations.
Synergies were significantly reworked in the Advanced Gungeons & Draguns Update. Along with numerous synergies being introduced and synergies made easier to acquire, every synergy was given a name and players can check what currently held items are synergizing in the Ammonomicon by selecting an item in a synergy, highlighting the other items in the synergy. 2ff7e9595c
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