Value co-creation underpins the symbiotic relationships of digital ecosystems. Many IS studies have focused on sponsored co-creation facilitated by a focal firm. There is a limited understanding of how co-creation can happen in an ecosystem without a focal firm. Building on an in-depth case study, we elaborate on the theoretical notion of autonomous digital co-creation. We propose two contributions: (1) In comparison to the centralized, collaborative, and facilitated nature of sponsored co-creation, our findings reveal the nature of autonomous digital co-creation characterized as distributed, cooperative, and emergent; (2) through an ecosystem-based analysis, this study extends the predominantly dyadic focus of existing value co-creation literature.

This article studies a time-segmented design method to achieve prescribed-time formation control (PTFC) while ensuring collision avoidance (CA) by introducing the virtual reference signal for multiagent systems (MASs). Simultaneously achieving these control objectives is challenging, especially in situations where there is a conflict between PTFC and CA. Three different Lyapunov functions (LFs) in the steady-state and transient-state stages play an important role in analyzing the prescribed-time convergence and collision-free characteristics, where the prescribed time can also be set in advance and is not affected by the initial values and parameters of MASs. In addition, the mismatching terms in transient-state stages are dealt with by the designed event-triggered mechanism (ETM), which makes this control strategy more intelligent and determines whether MASs should perform the formation task or CA according to the relationship between the real-time distances of the agents. The conclusion is extended to a class ofn-order MASs, where they can compensate for the mismatching terms and adjust the transient behaviors by the backstepping method and high-order filter, rather than designing distinct controller forms for each of these tasks. Finally, the validity of the proposed control strategy is verified through simulation examples.

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update is a key component in multi-objective EAs (MOEAs), and it is performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the first population-size ranked solutions (based on some selection criteria, e.g., non-dominated sorting, crowdedness and indicators) from the collections of the current population and newly-generated solutions. In this paper, we question this practice. We analytically present that introducing randomness into the population update procedure in MOEAs can be beneficial for the search. More specifically, we prove that the expected running time of a well-established MOEA (SMS-EMOA) for solving a commonly studied bi-objective problem, OneJumpZeroJump, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed stochastic population update method. This work is an attempt to challenge a common practice for the population update in MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.

Subset selection, which aims to select a subset from a ground set to maximize some objective function, arises in various applications such as influence maximization and sensor placement. In real-world scenarios, however, one often needs to find a subset which is robust against (i.e., is good over) a number of possible objective functions due to uncertainty, resulting in the problem of robust subset selection. This paper considers robust subset selection with monotone objective functions, relaxing the submodular property required by previous studies. We first show that the greedy algorithm can obtain an approximation ratio with respect to the correlation and submodularity ratios of the objective functions; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets. We prove that EPORSS can also be theoretically grounded, achieving a similar approximation guarantee to the greedy algorithm. In addition, we derive the lower bound of the correlation ratio for the application of robust influence maximization, and further conduct experiments to validate the performance of the greedy algorithm and EPORSS.

With the frequent occurrence of large-scale disasters, such as landslide and earthquake, timely and effective emergency resource scheduling becomes more and more important. Lots of disasters need multi-period rescue to satisfy the demand of disaster areas. In order to find a better plan to achieve the multi-period disaster relief, in this paper, a multi-period emergency resource scheduling problem is solved using the multi-agent genetic algorithm (MAGA) considering the uncertainty of traffic. The experimental results show that multi-agent genetic algorithm is more effective than genetic algorithm (GA) for this problem and it has better convergence.