Series of minor paper updates

paper/paper.bib

@@ -119,14 +119,15 @@ @article{leon2022multi
 }
 
 @article{taberna2023uncertainty,
-  title={Putting renewable energy auctions into action--An agent-based model of onshore wind power auctions in Germany},
-  author={Anatolitis, Vasilios and Welisch, Marijke},
-  journal={Energy Policy},
-  volume={110},
-  pages={394--402},
-  year={2017},
-  publisher={Elsevier},
-  doi = {10.1016/j.enpol.2017.08.024}
+  title={Uncertainty in boundedly rational household adaptation to environmental shocks},
+  author={Taberna, Alessandro and Filatova, Tatiana and Hadjimichael, Antonia and Noll, Brayton},
+  journal={Proceedings of the National Academy of Sciences},
+  volume={120},
+  number={44},
+  pages={e2215675120},
+  year={2023},
+  publisher={National Acad Sciences},
+  doi = {10.1073/pnas.2215675120}
 }
 
 @article{ghanem2022balancing,

paper/paper.md

@@ -62,7 +62,7 @@ Agent-based models (ABMs) are composed of autonomous heterogeneous agents intera
 However, before Mesa, there was no modern Python-based framework for ABMs that integrated with the scientific Python ecosystem. Since its creation in 2014, Mesa has been applied to modeling everything from economics and sociology to ecology and epidemiology and has been cited in more than 500 papers and 800 authors. With its most recent major release, Mesa has advanced usability and stabilized functionality. These features include enhanced management of agents, data collection advancements, an improved visualization framework, and making it easier for researchers to create and analyze complex simulations.
 
 # Core capabilities
-Mesa is a Python-based framework for ABM that provides a comprehensive set of tools for creating, running, and analyzing ABMs. Mesa integrates with the wider scientific Python ecosystem with libraries such as [NumPy](https://numpy.org/), [pandas](https://pandas.pydata.org/), [Matplotlib](https://matplotlib.org/), [NetworkX](https://networkx.org/), and more. Mesa is implemented in pure Python (3+) with a modular architecture comprised of three main components:
+Mesa is a Python-based framework for ABM that provides a comprehensive set of tools for creating, running, and analyzing ABMs. Mesa integrates with the wider scientific Python ecosystem with libraries such as [NumPy](https://numpy.org/), [pandas](https://pandas.pydata.org/), [Matplotlib](https://matplotlib.org/), [NetworkX](https://networkx.org/), and more. The backend of the framework is written in Python, while the front-end end uses a Python implementation of React. The modular architecture is comprised of three main components:
 
 1. Core ABM components (*i.e.,* agents, spaces, agent activation, control over random numbers)to build models
 2. Data collection and support for model experimentation
@@ -171,7 +171,7 @@ Mesa also includes experimental support for next-event time progression through
 ```
 
 ## Visualization
-Mesa’s visualization module, [SolaraViz](https://mesa.readthedocs.io/latest/tutorials/visualization_tutorial.html), allows for interactive browser-based model exploration. Advancements with Mesa 3 update the visualization from harder-to-maintain custom code to Solara, a standardized library. Usage of the visualization module can be seen below:
+Mesa’s visualization module, [SolaraViz](https://mesa.readthedocs.io/latest/tutorials/visualization_tutorial.html,  allows for interactive browser-based model exploration. Advancements with Mesa 3 update the visualization from harder-to-maintain custom code to [Solara](https://solara.dev/), a standardized library. Usage of the visualization module can be seen below:
 
 ```python
     visualization = SolaraViz(
@@ -258,6 +258,6 @@ Mesa has grown into a complete ecosystem with extensions including:
 The advancements leading to Mesa 3 were developed by six maintainers (the authors) and an active community with over 140 [contributors](https://github.com/projectmesa/mesa/graphs/contributors). We would especially like to thank [David Masad](https://github.com/dmasad) for his foundational work on Mesa.
 
 # Conclusions
-Mesa 3 introduces significant advancements to the Python ABM framework, enhancing the core toolkit with greater control, interactivity, and speed for researchers. These notable improvements, paired with its foundational integration with the scientific Python ecosystem, modular architecture, and active community, make it an indispensable tool for researchers across disciplines working in Python who need to create and analyze agent-based models.
+Mesa 3 introduces significant advancements to the Python ABM framework, enhancing the core toolkit with greater control, interactivity, and speed for researchers. These notable improvements, paired with its foundational integration with the scientific Python ecosystem, modular architecture, and active community, make it an indispensable tool for researchers across disciplines working in Python who want to create and analyze agent-based models.
 
 # References