Decision-makers often have to act before critical times to avoid the collapse of ecosystems using knowledge \textcolor{red}{that can be incomplete or biased}. Adaptive management may help managers tackle such issues. However, because the knowledge infrastructure required for adaptive management may be mobilized in several ways, we study the quality and the quantity of knowledge provided by this knowledge infrastructure. In order to analyze the influence of mobilized knowledge, we study how the following typology of knowledge and its use may impact the safe operating space of exploited ecosystems: 1) knowledge of the past based on a time series distorted by measurement errors; 2) knowledge of the current systems’ dynamics based on the representativeness of the decision-makers’ mental models of the exploited ecosystem; 3) knowledge of future events based on decision-makers’ likelihood estimates of extreme events based on modeling infrastructure (models and experts to interpret them) they have at their disposal. We consider different adaptive management strategies of a general regulated exploited ecosystem model and we characterize the robustness of these strategies to biased knowledge. Our results show that even with significant mobilized knowledge and optimal strategies, imperfect knowledge may still shrink the safe operating space of the system leading to the collapse of the system. However, and perhaps more interestingly, we also show that in some cases imperfect knowledge may unexpectedly increase the safe operating space by suggesting cautious strategies.
The code enables to calculate the safe operating spaces of different managers in the case of biased and unbiased knowledge.

The model is a stylized representation of a social-ecological system of agents moving and harvesting a renewable resource. The purpose is to analyze how mobility affects sustainability. Experiments changing agents’ mobility, landscape and information governments have can be run.

A thermostat is a device that allows to have the temperature in a room near a desire value.

The simulation generates two kinds of agents, whose proposals are generated accordingly to their selfish or selfless behaviour. Then, agents compete in order to increase their portfolio playing the ultimatum game with a random-stranger matching.

Under the Kyoto Protocol, governments agreed on and accepted CO2 reduction targets in order to counter climate change. In Europe one of the main policy instruments to meet the agreed reduction targets is CO2 emission-trading (CET), which was implemented as of January 2005. In this system, companies active in specific sectors must be in the possession of CO2 emission rights to an amount equal to their CO2 emission. In Europe, electricity generation accounts for one-third of CO2 emissions. Since the power generation sector, has been liberalized, reregulated and privatized in the last decade, around Europe autonomous companies determine the sectors’ CO2 emission. Short-term they adjust their operation, long-term they decide on (dis)investment in power generation facilities and technology selection. An agent-based model is presented to elucidate the effect of CET on the decisions of power companies in an oligopolistic market. Simulations over an extensive scenario-space show that there CET does have an impact. A long-term portfolio shift towards less-CO2 intensive power generation is observed. However, the effect of CET is relatively small and materializes late. The absolute emissions from power generation rise under most scenarios. This corresponds to the dominant character of current capacity expansion planned in the Netherlands (50%) and in Germany (68%), where companies have announced many new coal based power plants. Coal is the most CO2 intensive option available and it seems surprising that even after the introduction of CET these capacity expansion plans indicate a preference for coal. Apparently in power generation the economic effect of CO2 emission-trading is not sufficient to outweigh the economic incentives to choose for coal.

Designed to capture the evolutionary forces of global society.

This paper tries to shed some light on the mutual influence of citizen behaviour and the spread of a virus in an epidemic. While the spread of a virus from infectious to susceptible persons and the outbreak of an infection leading to more or less severe illness and, finally, to recovery and immunity or death has been modelled with different kinds of models in the past, the influence of certain behaviours to keep the epidemic low and to follow recommendations of others to apply these behaviours has rarely been modelled. The model introduced here uses a theory of the effect of norm invocations among persons to find out the effect of spreading norms interacts with the progress of an epidemic. Results show that norm invocations matter. The model replicates the histories of the COVID-19 epidemic in various region, including “second waves” (but only until the end of 2021 as afterwards the official statistics ceased to be reliable as many infected persons did not report their positive test results after countermeasures were relieved), and shows that the calculation of the reproduction numbers from current reported infections usually overestimates the “real” but in practice unobservable reproduction number.

The agent-based model WEEM (Woodlot Establishment and Expansion Model) as described in the journal article, has been designed to make use of household socio-demographics (household status, birth, and death events of households), to better understand the temporal dynamics of woodlot in the buffer zones of Budongo protected forest reserve, Masindi district, Uganda. The results contribute to a mechanistic understanding of what determines the current gap between intention and actual behavior in forest land restoration at farm level.

The purpose of this model is to analyze the dynamics of endogenously created oscillations in housing prices using a system dynamics simulation model, built from the perspective of construction companies.

This is a multi-patch meta-population ecological model. It intended as a test-bed in which to test the impact of humans with different kinds of social structure.