Simulation games are designed to cultivate expertise and rehearse particular skill sets. To yield longitudinal effects, sequences of events must be crafted to yield intended learning outcomes, sometimes by focusing on particularly difficult situations and replaying variants. The present paper develops a logic-based approach for encoding the interrelation between action, events, and objects in a manner that allows the resulting scenario description to immediately be executed in a game development environment. This has the dual effect of decoupling the description of a scenario from the simulation platform itself, as well as supporting iterative and flexible development of learning content. To this end, we provide three interrelated components: First, we develop a scenario description language based on Answer Set Programming. The language is designed to allow an automated reasoner to deduce a schedule of the future events that are caused by an action taken in a given simulation environment. Second, we define a protocol for exchanging actions and computed futures between, respectively, the simulation environment and the external automated reasoner. Finally, as a proof of concept, we develop an Application Programming Interface (API) for the Unity Real-Time Development Platform that implements the protocol and offers a software framework for connecting the computed future events to concrete game objects. This allows the game to evolve coherently from the specification. We argue that the resulting system inherits capabilities for artificial commonsense reasoning from its declarative basis which are useful for reasoning about an evolving emergency incident or training scenario.

In this article, we give advice on transitioning to a more agile delivery model for large-scale agile development projects based on experience from the Parental Benefit Project of the Norwegian Labour and Welfare Administration. The project modernized a central part of the organization’s IT portfolio and included up to ten development teams working in parallel. The project successfully changed from using a delivery model which combined traditional project management elements and agile methods to a more agile delivery model with autonomous teams and continuous deployment. This transition was completed in tandem with the project execution. We identify key lessons learned which will be useful for other organizations considering similar changes and report how the new delivery model reduced risk and opened up a range of new possibilities for delivering the benefits of digitalization.

Predictions of time (e.g., work hours) are often based on the aggregation of estimates of elements (e.g., activities, subtasks). The only types of estimates that can be safely aggregated by summation are those reflecting predicted average outcomes (expected values). The sums of other types of estimates, such as bounds of confidence intervals or estimates of the mode, do not have the same interpretation as their components (e.g., the sum of the 90% upper bounds is not the appropriate 90% upper bound of the sum). This can be a potential source of bias in predictions of time, as shown in Studies 1 and 2, where professionals with experience in estimation provided total estimates of time that were inconsistent with their estimates of individual tasks. Study 3 shows that this inconsistency can be attributed to improper aggregation of time estimates and demonstrates how this can produce both over- and underestimation—and also time prediction intervals that are far too wide. Study 4 suggests that the results may reflect a more general fallacy in the aggregation of probabilistic quantities. Our observations are consistent with that inconsistencies and biases are driven by a tendency towards applying a naïve summation (2+2=4) of probabilistic (stochastic) values, in situations where this is not appropriate. This summation fallacy may be in particular consequential in a context where informal estimation methods (expert-judgment based estimation) are used.