The Future Of Creedible ((FULL))

With being the most trusted institution, business is obligated to join forces with the government to address challenges and map a clear vision for the future. The Trust Barometer data highlights clear avenues to do just that.

Recreating two cases that the literature already covers, however, is clearly insufficient to provide the clarification we seek: in a world that leaves open these two opposing futures, to which are we heading?

The Future Of Creedible

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Yet there is an important sense in which future contingents are not all alike: some of them express contents that is reasonable to believe, others do not. If you normally eat breakfast every morning, your breakfast supplies are safely stored in the kitchen, and you have no intention to change your usual routine, then you may reasonably believe what follows:

As far as truth values are concerned, (1) and (2) are exactly like (3) and (4), on any view of future contingents. According to Aristotelianism, both (1) and (2) lack truth value. According Peirceanism, they are both false. According to Ockhamism, one of them is true and the other is false. In each of the three cases, what holds for (1) and (2) also holds for (3) and (4).

Historically, the question whether future contingents are either true or false has been extensively discussed in connection with the issue of divine foreknowledge. Although we recognize the intrinsic theoretical interest of this issue, we think that modelling the cognitive behaviour of agents with limited epistemic resources is no less important than investigating the idea of an omniscient being. So, here we will stick with the finiteness constraint, and reason as if there is no God.Footnote 4

As it will turn out, our account of credibility satisfies the probability constraint insofar as it entails that credibility is itself a kind of probability. We take this to be a remarkable result, because it shows a convergence between a coherent formal treatment of the epistemology of future contingents and an independently grounded mathematical theory.

The second idea is that a branching time model provides an adequate representation of the spectrum of future possibilities that are open at a given moment. A branching time model is usually defined as a triple \(\langle M,

In order to define truth, we will rely on a specific view of future contingents, Ockhamism. According to Ockhamism, future contingents are either true or false, although they are neither determinately true nor determinately false. Their truth or falsity depends on what happens in the actual history. That is,

The thought that underlies (T) is that, when a future contingent is uttered at m, the utterance involves reference to one in particular among the many courses of events that are possible at m, the actual course of events. Therefore, a formal semantics in which truth is defined for a set of histories can provide a characterization of plain truth to the extent that one of the histories in the set represents that course of events. The truth value of \(\alpha \) at m, indicated as \(T(\alpha )_{m}\), can be defined as follows, assuming that one of the histories in \(H_{m}\) is the actual history:

hrstrm and Hasle (2020) provides a detailed historical reconstruction of the debate on future contingents. Todd and Rabern (2021) is a recent work on the relation between future contingents and temporal omniscience.

It is worth mentioning that, if one were to adjust clause 6 of Definition 3 in the way explained in footnote 12, one would end up with the unpalatable result that every future-tense sentence has credibility 0 or 1, without intermediate values. Todd (forthcoming) discusses the implications of this result.

Iacona (2021) advocates the view that future contingents are knowable and shows how we can make sense of their knowability within an Ockhamist framework. Cariani (forthcoming) argues in a similar spirit that the openness of the future does not prevent future contingents from being knowable.

In the most recent literature on future contingents, the issue of assertibility has been widely discussed, see Perloff et al. (2001), Stojanovic (2014), MacFarlane (2014), Hattiangadi and Besson (2014) and Santelli (2020).

The advent and distribution of vaccines against SARS-CoV-2 in late 2020 was thought to represent an effective means to control the ongoing COVID-19 pandemic. This optimistic expectation was dashed by the omicron waves that emerged over the winter of 2021/2020 even in countries that had managed to vaccinate a large fraction of their populations, raising questions about whether it is possible to use scientific knowledge along with predictive models to anticipate changes and design management measures for the pandemic. Here, we used an extended SEIR model for SARS-CoV-2 transmission sequentially calibrated to data on cases and interventions implemented in Florida until Sept. 24th 2021, and coupled to scenarios of plausible changes in key drivers of viral transmission, to evaluate the capacity of such a tool for exploring the future of the pandemic in the state. We show that while the introduction of vaccinations could have led to the permanent, albeit drawn-out, ending of the pandemic if immunity acts over the long-term, additional futures marked by complicated repeat waves of infection become possible if this immunity wanes over time. We demonstrate that the most recent omicron wave could have been predicted by this hybrid system, but only if timely information on the timing of variant emergence and its epidemiological features were made available. Simulations for the introduction of a new variant exhibiting higher transmissibility than omicron indicated that while this will result in repeat waves, forecasted peaks are unlikely to reach that observed for the omicron wave owing to levels of immunity established over time in the population. These results highlight that while limitations of models calibrated to past data for precisely forecasting the futures of epidemics must be recognized, insightful predictions of pandemic futures are still possible if uncertainties about changes in key drivers are captured appropriately through plausible scenarios.

The resurgence of the pandemic over the winter of 2021 and early 2022 has, secondly, also refocused attention on whether it is possible to forecast the future stages of a contagion reliably using mathematical models [10, 11]. Thus, while some workers have highlighted the difficulty of anticipating and accommodating novel, unknown, or previously unsuspected changes in the drivers of future viral transmission to allow the making of reliable long-term projections by models [10, 11], others have pointed to the value of these models as tools for being able to integrate information on the diverse structures and processes related to transmission dynamics in order to propagate forecasts that are more accurate than predictions afforded by common sense alone [12, 23]. Such assessments have also, for example, pinpointed the need for continual model refinement and for the use of data for making predictions to counter the effects of changes in local risk factors [12, 14, 24]. These studies ultimately suggest that, as for other studies investigating socio-ecological futures, the better use of models for forecasting the plausible futures that could be followed by the pandemic is to combine simulations within a scenario framework in order to focus on explorations of possible trajectories in the evolution of the system as a result of changes predicted for key drivers rather than employing them to make precise predictions about the extent or duration of disease burdens [25, 26].

The coupled differential equations governing the evolution of the full extended system, the model code used to perform the simulations, and all prior and posterior fitted parameter values for the best-fit models calibrated to data to Sept. 24th 2021 are given in the Table provided at -FL-Vaccination. The ensemble of best-fitting models obtained from the sequential model calibrations was used to forecast the impacts of the various future scenarios related to immunity durations and the advent and spread of new variants explored in this paper. Fig 1 provides a flowchart of the full structure of the extended SEIR model described above.

The probability of pandemic fade-out was assessed via simulation as follows. First, we used the ensemble of models that best fit the latest data (Sept. 24th 2021 in this case) to generate forward trajectories for the pandemic. For a given timestep, we then computed the fraction of those trajectories that showed strictly decreasing cases into the future. A trajectory is considered decreasing if their predicted cases are currently higher than they will be one week in the future; this weekly assessment also ensures that daily fluctuations in cases are ignored. The fraction of such trajectories is used directly to calculate the probability of elimination of the pandemic over the chosen timestep. This analysis was performed for the case of continued social distancing measures and vaccination, and under the conditions of full release of social measures. These estimations of fade-out probabilities were carried out for the case of long-term immunity and for immunity that persisted for at least 2.5 years (see results).

We examined the effect of long-term immunity on the future path (to end of 2022) that may be followed by the pandemic by combining the models parameterized using data to Sept. 24th 2021 with scenarios that included the operation of long duration immunity but which differed in the levels of social protective measures and vaccinations implemented. These simulations firstly show that the delta variant-induced wave of the pandemic peaked on Aug. 26th 2021 at 22,400 median daily cases in line with case reports (Table 2), with cases declining thereafter under the levels of social mitigation (23%) and vaccinations (20,000/day) observed to Sept. 24th 2021 (Fig 2B). If immunity to SARS-CoV-2 is long-term, the predictions for this scenario also indicate that the pandemic will fade out in early 2022 (see below). The solid blue curve shows that fully releasing social protection measures from Sept. 24th 2021 will result in only a small increase in cases (over those produced under maintaining the social protective measures observed around Sept. 24th 2021), which will subsequently decline to small levels from July 2022. It is notable that in direct contrast, if social mitigation measures had been released on Mar. 1st 2021, a major spike in cases would have occurred (blue dashed curve). Increasing the vaccination rate 1.5x from Sept. 24th 2021 (to approximately mimic the school vaccinations that were being proposed then) under maintenance of the then observed social protective measures would have resulted in lower future cases but not significantly so compared to the predictions for the pandemic future given continuance with these social measure/vaccination levels (green dashed curve). Releasing such social mitigation measures fully while increasing the vaccination rate by 1.5x, however, would have resulted in an increase in cases but this increase would only be slightly lower than that predicted for when the observed vaccination rate is continued (dashed magenta curve and Table 2).These results indicate that releasing social measures fully and increasing the vaccination rate from Sept. 24th 2021 would have only a moderate impact on the future course of the pandemic under conditions of permanent immunity. be457b7860